Approved and recommended for acceptance as a thesis in partial fulfillment of the
requirements for the degree of Master of Science.
Special committee directing the thesis of James Keith Whalen
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Thesis Co-Advisor
Date
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Thesis Co-Advisor
Date
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Received by the College of Graduate and Professional Program
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Date
A Risk Assessment for Crayfish Conservation on National Forest Lands in the Eastern
United States
James Keith Whalen
A thesis submitted to the Graduate Faculty of
JAMES MADISON UNIVERSITY
In
Partial Fulfillment of the Requirements
for the degree of
Master of Science
Department of Biology
May 2004
Acknowledgements
I would like to thank Mr. Mark Hudy for giving me the opportunity to work on
this project, and in the process help me become a better fish biologist and aquatic
ecologist. His insight and guidance in writing and public speaking has and will continue
to be invaluable. I am also grateful to Mark and Dr. Andy Dolloff of the USDA Forest
Service Aquatic Ecology Unit and the Center for Aquatic Technology Transfer for
funding my research these past 2 ½ years. Thanks to, Dr. Reid Harris for agreeing to be
my co-major advisor and for the advice and helpful input that he provided during my
time at James Madison University. My other committee members Dr. Jon Kastendiek
and Dr. Grace Wyngaard provided scientific advice and acted as a sounding board for my
ideas (no matter how crazy). I appreciate Dr. Rickie Domangue from the Math
department at James Madison University for providing statistical assistance during data
analysis. I’m grateful to the faculty, staff, and fellow graduate students in the Biology
department at James Madison University that gave feed back on my project or created a
friendly atmosphere during my stay.
I would like to thank all the people who worked in our lab over the past two years
who have helped enter and analyze data, especially, Giancarlo Cesarello for helping to
develop most of my GIS data layers. Pauline Adams worked on developing the crayfish
database and data analysis of the Land cover/Land Use data and got me out of the office
when I needed it most. Thanks to Teresa Thieling and Seth Coffman for reviewing
sections of my thesis and for answering my crazy questions through out the writing
process.
ii
I would like to thank Karen Reed from the Smithsonian Institute National
Museum of Natural History for taking the time to gather the Smithsonian’s crayfish data
for Alabama, Kentucky, Mississippi, and South Carolina. I am grateful to Dr. Schuster
and Dr. Taylor for providing their unpublished data for Kentucky, and Dr. Nuttall for
providing his unpublished data for Pennsylvania. I appreciate Dave Peterson for
providing the USDA Forest Service’s data for Texas, and Alan Clingenpeel and Rich
Standage for providing the USDA Forest Service’s data for Arkansas. Dr. Eric Smith’s
(Virginia Tech) and Dr. Robert Hilderbrand’s (University of Maryland) assistance were
instrumental in my analysis of the human population data.
I would really like to thank Robin Stidham who has been my good friend and also
the brains behind the operation at the USDA Forest Service who kept me on track and
made sure I got paid. She was one of the people who always believed in me. I am
indebted to Dr. Dolloff and his wife, the late Jinny Worthington, for giving me a helpful
push to go back to graduate school. Lastly, I would like to thank my family, especially
my mom and dad, for believing in me and for putting up with a son 29 year old son who
still has not figured out what he wants to be when he grows up.
iii
Table of Contents
Introduction………………………………………………………………………..
7
Methods……………………………………………………………………………
9
Watershed Scale……………………………………………………………
9
Study Area………………………………………………………………… 10
Dependent Variable……………………………………………………….. 11
Location Metrics…………………………………………………………… 12
Independent Variables……………………………………………………... 12
Projection and Datum……………………………………………………… 12
Location Metrics…………………………………………………………... 12
Independent Variables……………………………………………………... 12
Projection and Datum……………………………………………… 13
Dams……………………………………………………………….. 13
Roads………………………………………………………………. 14
Land Use…………………………………………………………… 14
Human Population…………………………………………………. 15
Air Pollution……………………………………………………….. 16
Metric Screening and Scoring……………………………………… 17
Final Risk Assessment/Scoring System…………………………………… 18
Crayfish Distributional Data……………………………………………….. 19
Results……………………………………………………………………………… 21
Crayfish Database………………………………………………………….. 21
Rarity-Weighted Richness Index…………………………………………... 21
iv
Location Metrics…………………………………………………..……….. 22
Metrics……………………………………………………………..………. 23
Weighted Values…………………………………………………..………. 25
Final Risk Assessment……………………………………………….….…. 25
Crayfish Distributional Data…………………………………………..…… 26
Discussion…………………………………………………………………..……… 27
Appendix………………………………………………………………………...…. 31
Bibliography……………………………………………………………………..… 117
v
Abstract
Risk assessments over large geographic areas are important tools for land
managers and decision makers in the planning, priority setting, and overall management
of public lands. Because of human population growth and changing land use practices,
lands in the eastern United States that are managed by the USDA Forest Service are
becoming increasingly important for the protection, restoration and enhancement of the
area’s aquatic resources. I developed a watershed based risk assessment for crayfish to
assist land managers and decision makers in the management of these lands. Crayfish
diversity in the eastern United States is the highest in the world and USDA Forest Service
lands are thought to be a key stronghold for this biodiversity. Crayfish are also important
as ecological indicators, as food resources, and are important economically as bait for
recreational and commercial fisheries. Crayfish distributional data and their potential
role as ecological indicators are currently not being utilized effectively in management of
USDA Forest Service lands. The risk assessment was conducted for 991 5th level
watersheds in the eastern United States that contain USDA Forest Service lands. This
watershed level risk assessment was based on a rarity-weighted richness index (RWRI)
constructed from presence/absence crayfish taxa data and 11 watershed level metrics
reflecting different anthropogenic impacts. I found 254 species/subspecies (taxa) of
crayfish present on the 991 5th level watersheds. The average watershed had 5.29 taxa
with a range from one to 21. Twenty -seven taxa were found in only one watershed. The
southeastern United States contained watersheds that have a high RWRI and can be
considered highly irreplaceable. The risk assessment showed that the southern
Appalachian watersheds and several watersheds in Ohio, Illinois, Indiana, and Missouri
v
were at the highest risk. Watersheds with high RWRI values and high risks from
anthropogenic factors should be of special concern to land managers.
vi
1
Introduction
The global loss of biodiversity is accelerating (Meffe and Carroll 1994; Forester
and Machlis 1996) and is of particular concern for aquatic organisms (Benz and Collins
1997; Master et al. 1998). Risk assessments at watershed scales over large geographic
areas have been part of recent aquatic conservation strategies to prioritize areas for
protection and restoration efforts (Davis and Simon 1995; Master et al. 1998; Moyle and
Randall 1998). Based on the biotic integrity of watersheds in the eastern United States
that contain USDA Forest Service lands I developed a risk assessment for crayfish
conservation on 991 watersheds. This assessment can be used to help federal land
managers set priorities for watershed restoration and land management planning and
practices.
Human effects are a major factor in the decline of aquatic biodiversity (Moyle and
Sato 1991; Allan and Flecker 1993; Williams et al. 1992; Forman 1995; Frissell and
Bayles 1996; Taylor et al. 1996), and several studies have examined the correlation
among taxa richness and anthropogenic influences (Forester and Machlis 1996; Findlay
and Houlahan 1997). Dams, roads, land use, human population density, and pollution are
highly correlated with the loss of species diversity (Benz and Collins 1997).
A debate has arisen over whether to protect all watersheds equally, (Dopplet et al.
1993) or to use a selected approach to watershed conservation (Power et al. 1994; Frissel
et al. 1996; Moyle and Randall 1998; EPA 2002a). Moyle and Randall (1998) used
specific criteria (water development, human use, and trout introduction) to develop an
index of biotic integrity (WIBI) for watersheds in the Sierra Nevada mountains. The
present study uses a similar approach to evaluate watersheds. Implicit in this approach is
2
that all watersheds may not be of equal importance from a conservation stand point and
that budget constraints may prevent universal protection and/or restoration of all
watersheds. Watershed metrics may be based on the presence and severity of certain
anthropogenic factors (e.g., dams, roads, land use, human population, and air pollutants)
and weighted based on their relationship to presence/absence of specific organism for the
risk assessment.
I chose crayfish as this organism because of 1) high species diversity in the
eastern United States, 2) the lack of large-scale distributional and ecological studies, 3)
the likely importance of watersheds within USDA Forest Service lands in the overall
conservation of crayfish species in the eastern United States, and 4) the ecological role of
crayfish in aquatic ecosystems. Crayfish are ecologically important in aquatic
ecosystems because they 1) represent a large proportion (>50%) of the biomass produced
in some aquatic systems (Rabeni 1992; Roell and Orth 1993; Griffith et al. 1994; Taylor
et al. 1996; Holdich 2002), 2) are significant shredders of vegetation and leaf litter,
critical in food chain processes (Huryn and Wallace 1987; Griffith et al. 1994; Taylor et
al. 1996; Holdich 2002; Adams et al. 2003), and 3) are important recreational and
commercial bait fisheries and food resources (Nielsen and Orth 1988; Taylor et al. 1996).
Previous studies of crayfish distribution have been at smaller scales (individual
watersheds or geographical areas) (Jezerinac et al. 1995; Koppelman and Figg 1995;
Crandall 1998; Robison 2001; Lewis 2002) or they have not involved analysis of
anthropongenic impacts (Williams et al. 1989).
Objectives of my study were to: 1) construct a presence/absence database of
crayfish in watersheds in the eastern United States that contain National Forest lands, 2)
3
use the database to develop a rarity-weighted richness index (RWRI), 3) develop a set of
metrics for watershed analysis using different anthropogenic factors, 4) correlate the
RWRI with the anthropogenic metrics to determine the strength of the relationships for
construction of the watershed risk assessment, 5) construct a watershed risk assessment
for crayfish conservation on National Forest watersheds, and 6) assess the current
knowledge of crayfish distribution by state and national forest.
Methods
Study Area
The study area includes 991 watersheds across twenty-six states and two National
Forest Regions (Southern and Eastern) (Figure 1). The size of the watersheds ranged
from 11 to 1,854 km2 with a mean size of 452 + 248 km2.
Watershed Scale
I chose 5th level Hydrologic Unit (HU) watersheds (16 – 102 thousand hectares)
(Seaber et al. 1987; McDougal et al. 2001; EPA 2002b; USGS 2002b) as the analysis
units for this risk assessment. The 5th level HU was chosen because: 1) it was the
smallest size where data were currently available, 2) it is a level of great interest for land
management (McDougal et al. 2001), and 3) it is a size where plans can be developed for
conservation management at a reasonable scale (Moyle and Yoshiyama 1994; Master et
al. 1998). The US Geological Survey (USGS) and the Natural Resource Conservation
Service (NRCS) are currently completing standardized 5th level HU watershed coverage.
Because 5th level HU watersheds have yet to be nationally standardized by USGS and
NRCS, I used draft 5th level HU delineations obtained from each National Forest in the
eastern United States or USGS and NRCS state offices. I assigned a unique identification
4
code to each watershed which I referred to as the watershed id. The new coding system
was cross-referenced with original identification codes so that watersheds could be
identified by either coding system at a later time.
Figure 1. States containing lands managed by the USDA Forest Service in the Eastern
United States. The small black polygons are the 5th level Hydrologic Units (watersheds)
that contain USDA Forest Service lands. The black line shows the division between the
Eastern and Southern USDA Forest Service Regions.
Dependent Variable
I used a combination of scientific articles, unpublished data, and expert opinion to
determine crayfish presence/absence for each HU (Table A1). Across all study
watersheds I found 256 taxa (species/subspecies). The number of taxa in each watershed
5
was retained as a potential dependent variable. Because of potential differences related to
geographical area and a desire to give rare and endangered taxa greater weight, I also
developed a rarity-weighted richness index (RWRI) score for each watershed as another
potential dependent variable. The RWRI concept was developed for terrestrial
vertebrates by Cstuti et al. (1997) and subsequently used by The Nature Conservancy
(Master et al. 1998) in an aquatic assessment of fish. I used a RWRI to determine how
uncommon the taxa in a HU are compared to other watersheds for each crayfish taxa in a
watershed. The index is calculated by taking one and dividing it by the total number of
watersheds in which each taxa is found. This gives a rarity value for each taxa of
crayfish. The total rarity value for each taxa found in each watershed is summed together
to obtain the final watershed RWRI score using the following equation:
RWRI = Σn i =1(1/hi)
where n = the number of taxa found within a watershed, hi = the total number of
watersheds in which the taxa currently occurs, and i = the individual watersheds. For
example, a watershed with two taxa of crayfish, one that was found in 34 watersheds and
one that was found in only 2 watersheds would have a total RWRI of (1/34 + 1/2) or
0.529. A second watershed with two taxa of crayfish with each found in only 2
watersheds would have a total RWRI of (1/2 + 1/2) or 1.000. The rarity of crayfish taxa
in the second watershed is greater. I computed a RWRI score for each HU based on taxa
presence/absence. I used this score as an index of irreplaceability for crayfish taxa in the
watershed (Master et al. 1998). For example, a rare crayfish population found in only
one watershed is at a higher risk of extinction. This means that the watershed were this
rare species is found is highly irreplaceable. Another watershed that contains only one
6
species, but that species is very common is highly replaceable. This process is all relative
to the scope of the study.
Location metrics
I sorted watersheds by quartiles for geographic location (latitude, longitude, and
elevation) and the percentage of USDA Forest Service ownership to determine these
variables role in the distribution of crayfish diversity and taxa richness for my entire
dataset (Freedman et al. 1992; Taylor and Gaines 1999; Bromham and Cardillo 2003;
Willig et al. 2003; Zapata et al. 2003). I compared the mean value from the upper
quartile and the mean values from the lower quartile for each of my dependent variables
using a t-test. If a gradient existed, I would expect to see a significant difference
(p<0.05) between the mean values in the lower and upper quartiles. Additionally, I
determined the correlation of the dependent variable candidates with each of the
geographic location metrics (latitude, longitude, elevation) and the percentage of USDA
Forest Service ownership by determining Pearson correlation coefficients. I would
expect to see high correlations between the location metrics and the dependent variable
candidates if a gradient exist.
Independent Variables
The independent variables (dams, roads, land use, and human population density)
were obtained and/or developed as a Geographic Information System (GIS). The
development of a GIS allows for data analysis in a spatial context (Luo and Yueng 2002).
All databases were at the same resolution with common definitions for all 991
watersheds. This least common denominator requirement eliminated many potential
databases from consideration but allowed for the development of metrics that could be
7
used across large scales. Each of the metrics were analyzed in all the 5th level HU
watersheds (n = 991). Several of the metrics were analyzed on a per unit area basis
because of the large area differences among 5th level HU watersheds, while others were
given as percentages of the watershed.
Projection and Datum
All the spatial data obtained for the study was converted to a common projection.
I used Albers Equal Area/Conical because this conic projection uses two standard
parallels to reduce distortion. Albers is often used in small regional and national maps
and for analysis at these scales (Lo and Young 2002). All data were also converted to a
common datum. The North American Datum from 1983 (NAD83) was used for the
study because it is the local datum used for North America. It was developed in 1983
using satellites and ground based measurements. NAD83 uses the GRS 80 ellipsoid as
the reference surface (Doyle 1997; Lo and Young 2002). In this study, all data were
converted to the Albers projection and NAD83 datum.
Dams
Information on dams was obtained from the National Inventory of Dams (NID)
developed by the United States Army Corps of Engineers (1998). This database contains
the location of 75,187 dams in the contiguous United States in addition to the size of the
dam, size of the impoundment, ownership, and year of construction. The United States
Army Corps of Engineers and the Federal Emergency Management Agency developed
the NID to keep track of dam condition and potential hazard areas from dam failures.
The database was established in 1999 with 1998-1999 data and is updated quarterly. I
used the data that was available on the website in December of 2002. Several metrics for
8
dams were considered for use in the risk assessment including: total number of dams per
land area per watershed (dam_a) , total surface area of reservoirs per land area per
watershed (dam_sa_a), total storage volume of reservoirs per land area per watershed
(dam_v_a), number of dams over 50 feet in height per land area per watershed
(dam_50h_a), and number of dams built after 1950 per land area per watershed
(dam_50_a). Total number of dams was used in the final risk assessment because the
other attributes did not contain complete information for all dams.
Roads
Data on roads was developed using improved Topological Integrated Geographic
Encoding and Referencing system (TIGER) data (Navtech 2001). This database was then
analyzed using GIS programs called ros.aml (ROS) and watershed.aml (WATERSHED).
The ROS program splits the National Hydrologic Dataset (NHD) stream layer into line
segments that correspond to channel gradients (Cikanek 1997). WATERSHED uses the
spatial data from a Digital Elevation Model (DEM), 5th level HU coverage, the outputted
stream coverage from ROS, and the Navtech roads data to compute metrics related with
streams, gradient, and roads (Cikanek 2001). WATERSHED output data includes area in
the watershed (total, land, and lake), stream/road crossings (total, per stream, by
gradient), and road density (total, by distance from stream, by gradient). A list of the
codes, descriptions, and output produced from WATERSHED is given in Table A2.
Land Use
National land cover data (NLCD) was obtained from the USGS (USGS 2002a).
The NLCD data were produced using satellite imagery data acquired in 1992. These data
were used to establish a land-use/land-cover 30 m grid coverage. The individual blocks
9
in the grid were given a value that corresponded to a specific major land cover type. I
calculated the following metrics for land use per watershed: percentage of each individual
cover class (open water, high intensity residential, deciduous forest, pasture/hay, etc.),
percentage of each class group (water, developed, forested, agriculture, etc.), percentage
of native land cover (forested, native grassland, native shrubland, water, wetlands, bare
rock/sand/clay), and percentage of non-native land cover (agriculture, urban, mine, and
transitional areas). Land use/land cover values, definitions for each cover type, the subgroup and group that each cover type is part of is found in Table A3.
Human Population
I acquired county level human population census data from 1790-2000 to
construct watershed level metrics related to human population and population growth
(Geolytics 2000; US Census Bureau 2002). The human population data were used to
design separate GIS population grid coverages for each decade. The coverage was a
representation of the study area and was divided into grids that were one kilometer
squares. For a baseline, I used the 1990 census block data, a one kilometer grid coverage,
developed by Price (2003). Census block data, developed for the first time in 1990, was
used to establish the percentage of each counties population that was represented by each
one kilometer grid. I used this base percentage grid coverage and historic county level
census data to assign population numbers to each decade grid (Dr. Robert Hilderbrand,
University of Maryland, personal communication).
To convert the county level population to watershed population, I overlaid the
county grid coverages for each decade with the watershed coverage and summed the
common grids to calculate the watershed population and population density. I analyzed
10
the growth rates for three different time frames (current 1990 – 2000, recent 1950 – 1990,
and historic 1790 – 1940) and assigned a high rating (H) for watersheds with growth rates
in the upper quartile, a low rating (L) for watersheds in the lower quartile, and a medium
(M) rating for all other watersheds (Dr. Eric Smith, Virginia Tech, personnel
communication). A three letter code was assigned to each watershed based on the score
for each time frame (Table A7). The code indicates the human population growth rate
(high, medium, or low) over the three time periods. Because the simple linear regression
that I will be using to determine my weighting factors requires numeric values, each code
was given a numeric value with HHH getting a value of 25 and LLL getting a value of 1
(Table A7). These numeric values correspond to the watershed with the highest risk from
human population being assigned the higher values (closer to 25) and watershed with the
lowest risk from human population assigned the lower values (closer to 1). The
population code system (pop_cat) and the human population density for 2000 (den_2000)
were kept as possible metrics for the final watershed risk assessment.
Air Pollution
Air pollution metrics were derived from the 1999 nitrate and sulfate deposition
(kg/ha) data (National Atmospheric Deposition Program 2003). The coverages were
based on isopleths that were developed from set sampling points and each isopleth class
represented a range of deposition (kg/ha) values. I converted the coverage into a 300 m2
grid coverage for the study area. The nitrate and sulfate coverages were combined with
the watershed coverage to obtain the percentage of each class that was present in each
watershed. The percentage of each isopleth class was multiplied by the average value for
the kg/ha range for that class to approximate average nitrate/sulfate (kg/ha) value for each
11
watershed. I used the average nitrate (no_3_dep) and average sulfate (so_4_dep)
deposition value for each watershed as potential metrics in the risk assessment.
Metric Screening and Scoring
Candidate metrics were screened according to similar methods given in Hughes et
al. (1998) and McCormick et al. (2001) (Table A6). Metrics were tested for 1)
completeness, 2) redundancy, 3) relationship to the dependent variable, and 4) range.
First, metrics were eliminated if data were not available for the entire study area or if data
for the study area were not complete. Second, I eliminated redundancy by using
professional judgment to decide which single metric was retained when a correlation
(R>0.75) occurred among metrics. Next, metrics with a small range of values were
eliminated because they would not be useful in separating watersheds. Lastly, the
relationship of each metric to the dependent variable was tested. Only metrics with a
negative effect on the dependent variable candidates were kept as final metrics.
The range of conditions for each final metric was determined for all watersheds
and then a score was assigned to each watershed for each metric based on a quartile
system (Davis and Simon 1995; Barbour et al. 1999; Klemm et al. 2002. The scoring
system allowed for all the metrics to be ranked on the same set of scores (1-4) based on
the range of all watersheds. Metrics were given a score of one if they fell in the 25th
percentile (lower quartile), two if they fell between the lower quartile and the median,
three if they fell between the median and the upper quartile, and four if the score fell in
the 75th percentile (upper quartile).
12
Final Risk Assessment/ Scoring System
In the final step of developing the risk assessment, I scored each metric
(independent variables) based on the strength of the correlation with the dependent
variable. I used simple linear regression analysis to determine the strength of the
relationship between the dependent variable and each independent variable. The
following assumptions must be met for regression to be used for the analysis 1) the
independent variables are fixed and predetermined by the investigator and measured with
negligible error, 2) the relationship between the independent variable and each dependent
variable are linear, 3) the residuals (difference between the actual value and the predicted
value) are normally distributed, 4) the variance of the residuals is constant and is
independent of the magnitude of the dependent or independent variable, and 5) the values
for the dependent variable are randomly selected from the population and are independent
of one another (Dowdy and Wearden 1991; Sokal and Rohlf 1995; Zar 1999). I tested
the first assumption by examining the metadata for the dams, roads, human population,
and air pollution data and found this data to be fixed and measured correctly. I checked
the linearity by graphing a scatter plot and fitting a line for each metric. All the final
metrics showed a linear relationship with the dependent variable. I plotted the residuals
against a normal curve to check for normality. The plot for the residuals for each of the
final metrics showed no departure from normality by visual inspection. I checked the
fourth assumption by plotting the residuals against the independent variable. The plots
showed that the variance of the residuals was constant for each metric. I used simple
linear regression because the metrics (independent variables) were highly autocorrelated
13
and because running step-wise multiple regression could give different results based on
the order in which I would have added variables (Cox 1968; Miller 1984).
2
The risk assessment used the coefficient of determination (R value) for each of
the independent variables from the individual linear regressions as weighting factors.
2
The R value is based on a ratio between zero and one and is an estimate of the amount
of variability explained by each of the independent variables divided by the total
variability in the dependent variable (Sokal and Rohlf 1981; Milton 1992). The R2 value
was then multiplied by 100 to give me a number (100R2) that was greater than one.
I developed the final scoring system for the watersheds by multiplying the
2
original score obtained for each metric by the 100R value for that independent variable
(Figure 2). The sum of these values for all the independent variables was then obtained
for each HU to give me a continuous scoring system for the watersheds (Davis and
Simon 1995; Barbour et al. 1999; McCormick et al. 2001; Klemm et al. 2002). The sum
of these values were placed in 5 categories based on a quintile system (similar to the
method used for the independent variables) that included very low risk, low risk, medium
risk, high risk, and very high risk. These categories place the watersheds in groups to
give managers a more simplified management tool (Barbour et al. 1999).
Crayfish Distributional Data
The initial gathering of the crayfish taxa distribution data offered a unprecedented
opportunity to look at the extent of the knowledge of crayfish distribution in the eastern
14
Independent variable
Dams (5)
Roads (13)
Land-cover/Land-use (28)
Human Population (2)
Air Pollution (2)
Metric
Screening
Final Metrics
Dams (1)
Roads (4)
Land-cover/Land-use (3)
Human Population (2)
Air Pollution (1)
Metric Score
Each metric was scored on
a scale of 1-4 based on
quartiles of all watersheds
Multiply each metric
score by the
corresponding
weighting factor
Watershed Score
The sum of the products
of each metric times its
weighting factor
Dependent variable candidates
Numbers of Crayfish Taxa
Rarity Weighted Richness Index
(RWRI)
Testing for geographic
location, rarity of taxa
effects, and normality
Dependent variable selection
RWRI0.15 = RWRI_BC
(Normalize the data)
Simple linear
regression between
RWRI_BC and each
final metric to obtain
R2 values
Weighting Factor
100R2 = R2 X 100
for each metric
(Done to get values
greater than 1)
Final Score
Each total score is given a
rank 1-4 based on quartile
values of the total scores
(very-low, low, medium,
high, and very-high risk)
Figure 2. A flow chart representing the development of the watershed risk assessment
from the independent and dependent variables to the final risk assessment score.
Numbers in parentheses indicate number of metrics at each step. Dotted/dashed text
boxes represent development of the metrics. Dashed text boxes represent
development of the weighting factors. Solid text boxes represent development of the
final risk assessment scores.
15
United States. Since most distribution data and publications were done at a state level I
decided to also look at the relevant data at a state scale. I looked for entire state
distributional publications or websites. For this analysis, I only used publications or
sources that referenced crayfish distribution across entire states or multi-state areas.
Results
Crayfish Database
There were 254 crayfish taxa distributed across 991 watersheds in the study area
(Figure 3; Table A4). The average number of taxa per watershed was 5.3 with a
standard deviation of 3.7. All watersheds had at least one taxa; the maximum was 21.
Several taxa had very wide distributions; the five most common taxa were: Cambarus
diogenes diogenes (265), Orconectes virilis (240), Procambarus acutus acutus (190),
Cambarus bartonii bartonii (188), and Orconectes rusticus (178). Twenty-seven taxa
were found in one watershed in the study area (Figure 4; Table A4).
Rarity-weighted Richness Index
The RWRI scores ranged from a minimum of 0.004 to a maximum of 5.763. The
average score for the watersheds (N = 991) was 0.258 with a standard deviation of 0.406.
The RWRI scores were also placed into 4 categories (highly irreplaceable, irreplaceable,
replaceable, highly replaceable) for analysis based on quartiles (Figure A1). Because the
RWRI scores did not have a normal distribution, I used a Box-Cox transformation (Box
and Cox 1964; Sokal and Rohlf 1995) to determine what transformation to use to acquire
a normal distribution. I transformed the data using RWRI0.15, which I referred to as
RWRI_BC. Based on visual observation, the data looked normally distributed. The
180
160
Watersheds
140
120
100
80
60
40
20
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
# of Taxa
Figure 3. Distribution of watersheds (n=991) by number of taxa.
16
610
11
-2
0
21
-3
0
31
-4
0
41
-5
0
51
-6
0
61
-7
0
71
-8
0
81
-9
91 0
-1
10 00
11
15 50
12
20 00
130
0
5
4
3
2
1
Crayfish Taxa
50
45
40
35
30
25
20
15
10
5
0
# of Watersheds
Figure 4. Distribution of crayfish taxa (n=254) found by watersheds.
17
18
RWRI_BC score averaged 0.728 with a standard deviation of 0.162 (minimum = 0.432;
maximum = 1.300) (Table A5).
Location metrics
I found a latitudinal gradient in the number of crayfish taxa as well as the RWRI
and RWRI_BC. There were a significantly greater number of taxa in the south
(6.0/watershed) compared to the north (2.7/watershed)(p <0.001). I found a similar
relationship with the RWRI and RWRI_BC scores (p <0.001). Longitude was
significantly different (p <0.001) with crayfish taxa in the west (7.3) greater than in the
east (3.9). The same pattern was observed for the RWRI and RWRI_BC scores with
values in the watersheds in the east scoring lower than watersheds in the west (p <0.01).
Minimum, maximum, and mean elevation also showed a trend with lower elevations
having greater numbers of taxa, RWRI scores and RWRI_BC scores (p <0.042).
Percentage USDA Forest Service ownership was not significantly different for the
number of crayfish taxa (p = 0.661) and the RWRI_BC scores (p = 0.471). It was
significant for RWRI scores (p = 0.045) with higher scores in watersheds with greater
percentages of ownership (Table 1).
Similar results were obtained from comparing the Pearson correlation coefficients
for the correlation of latitude and number of taxa (R = -0.416) (Table 2). The negative
correlation showed a trend of higher numbers of crayfish taxa in the south shifting toward
lower numbers of taxa in the north. Differences were observed for latitude when
correlated with RWRI and RWRI_BC scores with higher values in the south compared to
the north. These observations confirm that a latitudinal gradient does exist in number of
crayfish taxa data as well as the RWRI and RWRI_BC scores. Differences were also
19
observed for longitude, minimum elevation, and mean elevation. Longitude showed a
gradient of higher numbers and values in the west and lower values in the east.
Minimum and mean elevation showed a trend of higher numbers of crayfish taxa and
higher RWRI and RWRI_BC scores at lower elevations. Significant correlations were
not observed for maximum elevation and percentage ownership. The results of the
correlation analysis contradict the results of the t-test for maximum elevation. There was
not a significant correlation between maximum elevation and number of taxa, RWRI or
RWRI_BC (Table 2).
The RWRI or RWRI_BC did not remove the geographical gradients in the data.
The number of taxa was significantly correlated with RWRI score (R = 0.578, p <0.001)
and RWRI_BC score (R = 0.741, p<0.001). To make sure that the transformation had not
changed the relationship of the data, I tested the correlation between RWRI and
RWRI_BC and found a significant correlation (R = 0.760, p<0.001). Even though
geographical gradients existed in the total number of taxa, the RWRI, and the RWRI_BC,
I used the RWRI_BC in the final risk assessment as my dependent variable because it
gives rare species greater weighting, it takes into consideration taxa abundance, and it
was normally distributed.
Metrics
By screening the metrics for data completeness, redundancy, range, and
relationship with RWRI_BC, I eliminated 39 and retained 11 metrics in the crayfish risk
assessment (Table A8). The watersheds were then given a score of 1-4 for each metric
based on quartiles. Lists of the range of values for each metric score are given in Table 3.
Table 1. T-test results comparing mean values for the number of crayfish taxa, Rarity-weighted Richness Index (RWRI) scores, and
Rarity-weighted Richness Index after transforming with the Box-Cox (RWRI_BC) scores in watersheds in the upper (south) or lower
(north) quartile for latitude along with the means for the same values using the upper and lower quartiles for longitude (east/west),
minimum elevation (low/high), maximum elevation (low/high), mean elevation (low/high), and percentage of ownership (low/high).
Location Metrics
Latitude
Longitude
Minimum
Elevation
Maximum
Elevation
Mean
Elevation
Percentage
Ownership
Dependent
Variable
Candidates
Crayfish taxa #
South
6.0
North
2.7***
East
3.9
West
7.3***
Low
6.5
High
4.5***
Low
6.1
High
5.4*
Low
6.4
High
4.9***
Low
5.2
High
5.1
RWRI
0.47
0.03***
0.17
0.25**
0.47
0.12***
0.49
0.18***
0.48
0.14***
0.27
0.20*
RWRI_BC
0.83
0.55***
0.70
0.74*
0.84
0.66***
0.83
0.72***
0.84
0.69***
0.73
0.72
*** p <0.001; ** p<0.01; * p<0.05
Table 2. Correlation Coefficients (R) for location metrics and dependent variable candidates.
Location Metrics
Dependent
Variable
Latitude Longitude Minimum Elevation
Maximum
Mean Elevation
Candidates
Elevation
Crayfish taxa #
-0.416**
0.314**
-0.127**
-0.015
-0.097**
RWRI
-0.396**
RWRI_BC
-0.704**
** p<0.01; * p<0.05
0.078*
0.086**
-0.254**
-0.290**
-0.051
-0.028
-0.242**
-0.244**
Percentage
Ownership
-0.026
-0.047
-0.037
20
21
Weighting Values
The R2 (amount of variability answered by each metric from the simple linear
regression) for each of the final 11 metrics ranged from 0.0001 (b30_rddn) to 0.029
(pop_cat) (Table 4). Four of the metrics had R2 values that were not significant. They
were retained in the final risk assessment because of they represented unique physical,
chemical, or biological metrics that were not eliminated in our screening process. These
metrics represent potential threats that may not be correlated with current crayfish
distributions. The 100R2 values were used as the weighting factor in the final risk
assessment. Final weighting values (100R2) values ranged from 0.1 to 29 (Table 4).
Table 3. The minimum and maximum values are given for each of the final 11 metric
scores (1-4) based on quartiles.
Scores
Metrics
1
2
3
4
dam_a
0 - 0.002
0.003 - 0.006
0.007 - 0.015
0.016 - 0.130
pop_cat
1.0 – 5.0
5.1 – 13.0
13.1 – 14.0
14.1 – 25.0
den_2000
0.0 – 5.0
5.1 – 11.0
11.1 – 24.0
24.1 – 308.0
road_den
0 - 0.96
0.97 - 1.30
1.31 - 1.75
1.76 - 12.15
xing_a
0 - 0.16
0.17 - 0.28
0.29 - 0.44
0.45 - 2.17
x1_2_stm
0 - 0.24
0.25 - 0.44
0.45 - 0.70
0.71 - 12.43
b30_rddn
0 - 0.67
0.68 - 1.06
1.07 - 2.01
2.02 - 10.11
no_3_dep
7.00 - 9.00
9.01 - 11.00
11.01 - 12.34
12.35 - 19.00
res_high
0 - 0.001
0.002 - 0.016
0.017 - 0.073
0.074 - 10.570
mine
0.000
0.001 - 0.002
0.003 - 0.048
0.049 - 13.540
human
0.31 - 6.88
6.89 - 13.85
13.86 - 26.82
26.83 - 93.37
Final Risk Assessment
The final risk assessment is calculated by multiplying the individual metric score
(1-4) for each metric by it’s corresponding weighting factor and then summing that value
for all the metrics in each watershed. Summed scores for the 991 watersheds ranged
from 114.1 to 448.4 with a median score of 283.3 (Table A9). Lower scores designated
watersheds with lower risk while higher scores designated watersheds with higher risk.
22
The scores were placed into categories of very low, low, medium, high, and very high
risk based on quintiles of their watershed scores. Each category represented
approximately 198 watersheds (Figure A2; Table 5).
Table 4. The R, R2, and 100R2 values for the final eleven metrics (Table A10).
100R2-value
Metric
R-value
R2-value
dam_a
-0.111
0.012***
12
***
pop_cat
-0.169
0.029
29
den_2000
-0.087
0.008**
8
road_den
-0.132
0.017***
17
xing_a
-0.132
0.017***
17
x1_2_stm
-0.024
0.001NS
1
NS
b30_rddn
-0.014
0.0001
0.1
no_3_dep
-0.129
0.017***
17
NS
res_high
-0.042
0.002
2
mine
-0.081
0.007*
7
NS
human
-0.042
0.002
2
*** p <0.001; ** p<0.01; * p<0.05; NS – not significant
Table 5. A range of watershed scores for the five final scoring categories, each category
represented 198 watersheds except for the medium category which represented 199
watersheds.
Category
Range
Very low
114.10 – 200.20
Low
200.21 – 257.10
Medium
257.11 – 307.20
High
307.21 – 359.40
Very high
359.41 – 448.40
Crayfish Distributional Data
Crayfish distributional data for each state for the year of publication ranged from
1906 to 2002. Three of the 26 states did not have a publication or other source of
information on crayfish distribution for the entire state (South Carolina, Alabama, and
Mississippi). Thirteen of the 26 states (50%) had a publication or other source of
information on crayfish distribution that had been published or released since 1980, and 9
of the 26 states (34.6%) had publication dates since 1990 (Figure A3; Table A1).
23
Discussion
Crayfish are distributed along a geographical gradient (decrease in numbers) from
north to south, east to west, and high to low elevation. Crayfish taxa are abundant and
rare increased in the southeastern United States. High taxa diversity in the southeastern
United States is also common with fish, aquatic salamanders, mussels, and aquatic insects
(Williams et al. 1992; Benz and Collins 1997; Warren et al. 2000; McDougal 2001).
This diversity is caused by the warm climate, diversity of habitats, complex landforms,
evolutionary history and lack of glaciation (Robison 1986; Warren et al. 1997; Master et
al. 1998). Land managers need to be aware of the high diversity in the southeastern
United States because these watersheds may be irreplaceable from a biodiversity
standpoint (Warren and Burr 1994; Warren 1996; Warren et al. 2000). Many
southeastern watersheds have both high diversity of crayfish and high anthropogenic
impacts which puts these watersheds in the very high risk category. Several watersheds
in Ohio, southern Illinois, and Missouri were also in the very high risk category (Figure
A2). The growing human population in these areas as well as air pollution from
neighboring areas are the major cause of these high ratings. There were high correlations
between the RWRI and the metrics of population growth, nitrate deposition, road density,
and road/stream crossings. Land managers need to implement effective best management
practices in these watersheds to decrease the effect of roads, dams, development, and
pollution. As they become available everywhere, the 1:24,000 streams layers, updated
roads layers, complete dams data, newer land use/land cover data, and pollution data for
point and non-point sources would strengthen future crayfish risk assessments.
24
The eastern United States does not have any species of exotic crayfish that have
been introduced from other countries. However, it does have several native species of
crayfish that have been introduced into other watersheds in the east. The rusty crayfish
(Orconectes rusticus) originated in Ohio, Kentucky, and Tennessee, but has spread by
bait introduction by anglers into Minnesota, Wisconsin, Michgan, New York, Vermont,
Maine, and many other states. Rusty crayfish out compete other native crayfish for food
and space and are a major threat to native species (Capelli 1982; Capelli and Munjal
1982; Hill and Lodge 1983; Lodge et al. 1986; Garvey et al. 1994). Areas in northeastern
Minnesota, upper peninsula of Michigan, and northern Wisconsin had very low risk
scores for the crayfish risk assessment. These same watersheds also had very low RWRI
scores. The importance of protecting these watersheds may be underestimated because of
the threat of introduced taxa. I do not have a good metric to determine the risk from
exotic species other than the surrogates of human population and high road density. An
improvement to the database would be to identify which species in a watershed are
introduced and which are native. A metric could then be developed for each watershed of
the number of introduced species that could be used in the final risk assessment.
In the National Forest of Florida, Alabama, and Mississippi, Louisiana, and
Arkansas, many of the watersheds have high RWRI scores. Some taxa in these
watersheds are only found in one or two watersheds, but the watersheds scored very low
for risk. This is because these watersheds are not currently at risk from the anthropogenic
factors that I included in my risk assessment. In priority setting, managers need to use
both the crayfish risk assessment and the RWRI as tools, because watersheds that are
currently not impacted by human influences could be protected first and may be the
25
watersheds of greatest importance for future protection of crayfish populations. It also is
hard to use a risk assessment at 5th level HU for taxa with very narrow ranges. Risk
assessments at 6th and 7th level HU, when they become available, could be more useful
when developing forest plans or writing environmental assessments for areas were taxa
with very small ranges exist. Biologists must use and update the database to make it a
dynamic tool for environmental assessments, biological assessments, forest plans, and to
track the introduction and/or extinction of species in a watershed (Warren 1996).
The current state of knowledge of crayfish taxa distribution is incomplete (Figure
A3). Many major state distribution publications and reports are outdated with publication
dates ranging from 1906 to 2002 with three states (Alabama, Mississippi, and South
Carolina) not having any state wide publication or report. The USDA Forest Service
should consider updating inventories and conducting research on crayfish distributional
ranges in the three states where no statewide publications exist (Alabama, Mississippi,
and South Carolina) or where statewide data are very old (Pennsylvania (1906), Michigan
(1931), New York (1957), Texas (1958), and Oklahoma (1969)). These inventories could
lead to point location data that could be used at multiple levels of analysis and could be
used in a GIS system to develop distributional probability models (LaHaye et al. 1994;
Muller-Edzards et al. 1997; Lehmann 1998; Brock and Owensby 2000; Gustafson 2001).
The probability models could in turn be used to prioritize sampling locations when trying
to locate additional populations of a given taxa of crayfish or to locate areas of habitat
that would be suitable for reintroduction.
Distributional knowledge and the risk assessment from this study will assist in the
conservation of crayfish on eastern National Forest lands. Many crayfish taxa are found
26
at there greatest abundance and largest population ranges on USDA Forest Service lands.
An expansion of the risk assessment to all watersheds in the east would most likely
highlight the importance of the watersheds with USDA Forest Service lands in
conservation of crayfish. It would show how low the anthropogenic effects are in
watersheds with USDA Forest Service in comparison to watersheds without Forest
Service ownership. It would also show how important the Forest Service watersheds at
being the last remaining population areas for rare crayfish species. This database can be
useful for land managers who are involved in writing environmental documents and
decision makers who are involved with natural resource planning. Currently, crayfish
distributional data are not included in the writing of many environmental documents.
27
Appendix
A
B
C
D
E
28
Figure A1. Maps of the crayfish Rarity-Weighted Richness Index (RWRI) scores for the study area. The study area is divided into
five regions: northwest (A), northeast (B), mid-atlantic (C), southwest (D), and southeast (E).
A
C
B
D
E
Figure A2. Maps of the crayfish final risk assessment scores for the study area. The study area is divided into five regions:
northwest (A), northeast (B), mid-atlantic (C), southwest (D), and southeast (E).
29
30
Figure A3. Decade of last statewide crayfish distributional publication or report for each
state in the study area. States in white were not included in the study area. The black
line designates the division of the eastern and southern USDA Forest Service regions.
31
Table A1. State publications, websites, and unpublished data used to develop the
crayfish presence/absence database. The date of last major state distributional
publication or report is also given.
State
Crayfish Database
Statewide
Sources
Publication
NONE
Alabama
Hall 1959; Hobbs and Hart 1959; Bouchard
1976; Fitzpatrick 1990; Butler 2002b; Reed
2003a
1980
Arkansas
Williams 1954; Reimer 1963; Fitzpatrick
1965; Hobbs 1977; Bouchard and Robison
1980; Hobbs and Robison 1985; Hobbs and
Brown 1987; Hobbs and Robison 1988; Hobbs
and Robison 1989; Robison 2000
1990
Florida
Hobbs 1942; Hobbs and Hart 1959; Franz and
Franz 1990; Hobbs and Hobbs 1991; Deyrup
and Franz 1994
Georgia
Hobbs and Hart 1959; Hobbs 1981; Butler
1981
2002b
Illinois
Brown 1955; Page 1985; Herkert 1992; Taylor
1985
and Anton 1999
Indiana
Eberly 1955; Page and Mottesi 1995
1995
Kentucky
1984
Maine
Ortmann 1931; Rhoades 1944b; Burr and
Hobbs 1984; Reed 2003b; Schuster and Taylor
2003
Penn 1950; Penn 1952; Penn 1956; Penn 1959;
Penn and Marlow 1959; Walls and Black 1991
Crocker 1979
1979
Michigan
Creaser 1931
1931
Minnesota
Helgen 1990
1990
Mississippi
New Hampshire
Fitzpatrick 1967; Fitzpatrick and Hobbs 1971;
Reed 2003c
Pflieger 1987; Missouri Department of
Conservation 1992; Pflieger 1996
Crocker 1979
New York
Crocker 1957; Crocker 1979
1957
North Carolina
Cooper and Braswell 1995; LeGrand and Hall
1995; Butler 2002b; Cooper and Schofield
2002; Fullerton 2002
2002
Louisiana
Missouri
1991
NONE
1996
1979
32
Table A1. continued.
State
Ohio
Oklahoma
Pennsylvania
South Carolina
Tennessee
Texas
Vermont
Virginia
West Virginia
Wisconsin
Crayfish Database
Sources
Turner 1926: Rhoades 1944a; Jerzerinaz 1982;
Jerzerinaz and Thoma 1984; Jerzerinaz 1986;
Jerzerinaz 1991; Thoma and Jerzerinaz 2000
Creaser and Ortenburger 1933; Dunlap 1951;
Reimer 1969; Bouchard and Bouchard 1976;
Robison 2001
Ortmann 1906; Schwartz and Meredith 1960;
Nuttall 2000; Nuttall 2003
Eversole 1995; Hobbs et al. 1976; Eversole and
Welch 2001a; Eversole and Welch 2001b; Butler
2002b; Reed 2003d
Ortmann 1931; Bouchard 1972; Thoma 2000;
Williams and Bivens 2001; Butler 2002a; Butler
2002b;
Penn and Hobbs 1958; Albaugh and Black 1973;
Hobbs 1990; Peterson 2003
Crocker 1979
Statewide
Publication
2000
Ortmann 1931; Thoma 2000; Butler 2002b;
McGregor 2002
Newcombe 1929; Schwartz and Meredith 1960;
Lawton 1979; Jerzerinac et al. 1995
Creaser 1932; Hobbs and Jass 1988
2002
1969
1906
NONE
1972
1958
1979
1995
1988
33
Table A2. Abbreviations for the output variables of the WATERSHED program
followed by a brief description of each attribute (*-considered for metrics) (original data
English units converted to metric units for project).
Codes
Description
ITEM
Watershed’s HU ID number
STRM_KM
Kilometers of stream in the watershed
SQ_KM
Total square kilometers of area in the watershed
LAKE_SQKM
Square kilometers of lakes within the watershed
LAND_SQKM
Square kilometers of land area within the watershed
ROAD_KM
Road kilometers within the watershed
RDKM/LND_SQKM * Road density in units of road kilometers/land square
kilometers within the watershed
SUM_CRSNGS
Total number of road/stream crossings within the
watershed
XNGS/L_SQKM *
Crossing density in crossings per land square kilometer
within the watershed
XNGS/ST_KM *
Crossings per stream kilometer within the watershed
XNGS_ST_0_1P
Total number of road/stream crossings on stream
segments with gradients between 0 and 1%
XNGS/KM_ST0-1P*
Crossings per kilometer of stream on segments with
gradients between 0 and 1%
XNGS_ST_1_2P
Total number of road/stream crossings on stream
segments with gradients between 1 and 2%
XNGS/KM_ST1-2P*
Crossings per kilometer of stream on segments with
gradients between 1 and 2%
B66_SQKM
Area in square kilometers within a 66 ft buffer of streams
within the watershed
B66LK_SQKM
Area in square kilometers of lakes within a 66 ft buffer of
streams within the watershed
B66LND_SQKM
Area in square kilometers of land within a 66 ft buffer of
streams within the watershed
B66_RD_KM
Road kilometers within a 66 ft buffer of streams within
the watershed
B66L_RD_DNS *
Road density in units of road kilometers/land square
kilometers within a 66 ft buffer of streams
STKM_W_RD66FT
Kilometers of stream with a road within a 66 ft buffer
distance of streams
PCT_ST_RD66 *
Percentage of streams with a road within a 66 ft buffer
distance of streams
B300_SQKM
Area in square kilometers within a 300 ft buffer of
streams within the watershed
B300LK_SQKM
Area in square kilometers of lakes within a 300 ft buffer
of streams within the watershed
B300LND_SQKM
Area in square kilometers of land within a 300 ft buffer of
streams within the watershed
34
Table A2. continued.
Codes
B300_RD_KM
B300L_RD_DNS *
STKM_W_RD300FT
PCT_ST_RD300 *
ST_GRD_0-1P_KM
ST_0-1P_B66__KM
PCT_ST0-1P_RD_B66 *
ST_0-1P_B300__KM
PCT_ST0-1P_RD_B300 *
ST_GRD_1-2P_KM
ST_1-2P_B66__KM
PCT_ST1-2P_RD_B66 *
ST_1-2P_B300__KM
PCT_ST1-2P_RD_B300 *
Description
Road kilometers within a 300 ft buffer of streams
within the watershed
Road density in units of road kilometers/land square
kilometers within a 300 ft buffer of streams
Kilometers of stream with a road within a 300 ft buffer
distance of streams
Percentage of streams with a road within a 300 ft
buffer distance of streams
Stream kilometers within the watershed with a gradient
between 0 and 1 %
Kilometer of 0 to 1 % gradient streams with a road
within a 66 ft buffer distance
Percentage of streams with a gradient between 0 and 1
% and a road within a 66 ft buffer distance
Kilometer of 0 to 1 % gradient streams with a road
within a 300 ft buffer distance
Percentage of streams with a gradient between 0 and 1
% and a road within a 300 ft buffer distance
Stream kilometers within the watershed with a gradient
between 1 and 2 %
Kilometer of 1 to 2 % gradient streams with a road
within a 66 ft buffer distance
Percentage of streams with a gradient between 1 and 2
% and a road within a 66 ft buffer distance
Kilometer of 1 to 2 % gradient streams with a road
within a 300 ft buffer distance
Percentage of streams with a gradient between 1 and 2
% and a road within a 300 ft buffer distance
35
Table A3. Class, class code, sub-group, group, and class definition for land use/land
cover attributes obtained from the National Land Cover Dataset.
Class
Class
Sub-Group
Group
Class
code
Definition
Open Water
11
Water
Natural All areas of open water
Low Intensity
21
Developed
Human Areas with a mixture of
Residential
constructed materials (3080%) and vegetation
High Intensity
22
Developed
Human Highly developed areas
Residential
were people live in high
densities; vegetation less
than 20%
23
Developed
Human Highly developed areas used
Commercial/
for infrastructure (roads,
Industrial/
railroads, etc.) and industry
Transportation
31
Barren
Natural Perennially barren areas of
Bare
bedrock, desert, talus, beach,
Rock/Sand/
and other accumulations of
Clay
earthen material
32
Barren
Human Areas of extractive mining
Quarries/Strip
activities with significant
Mines/Gravel
surface disturbance
Pits
Transitional
33
Barren
Human Areas of sparse vegetation
cover (<25%) that are
changing from one land
cover to another; usually
caused by human activity
Deciduous
41
Forested
Natural Areas dominated by tree
Forest
cover were 75% or more of
tree species shed foliage in
response to seasonal change
Evergreen
42
Forested
Natural Areas dominated by tree
Forest
cover were 75% or more of
tree species maintain their
leaves year round
Mixed Forest
43
Forested
Natural Forested areas were neither
deciduous or evergreen trees
make up 75% or greater of
the tree cover
Shrubland
51
Shrubland
Natural Areas dominated by shrubs;
usually as a natural
vegetation
61
Agriculture
Human Areas planted and
Orchards/
maintained for the
Vineyards/
production of fruits, nuts,
Other
berries, or ornamentals
36
Table A3. continued.
Class
Class
code
Grasslands/
71
Herbaceous
Pasture/Hay
81
Sub-Group
Group
Natural
Grassland
Agriculture
Natural
Human
Row Crops
82
Agriculture
Human
Small Grains
83
Agriculture
Human
Urban
Recreation
85
Developed
Human
Woody
Wetlands
91
Wetlands
Natural
Emergent
Herbaceous
Wetlands
92
Wetlands
Natural
Class
Definition
Areas dominated by upland
grasses and forbs
Areas of grass, legumes, etc.
planted for livestock grazing
or hay crops
Areas used for the
production of crops (corn,
soybeans, vegetables,
tobacco, cotton, etc.)
Areas used for the
production of grain crops
(wheat, barley, oats, rice,
etc.)
Vegetation in developed
settings planted for
recreation or aesthetic
purposes (parks, golf
courses, sports fields, etc.)
Areas were soil is
periodically saturated with
or covered with water and
25 – 100% of the cover is
trees or shrubs
Areas were soil is
periodically saturated with
or covered with water and
75 – 100% of the cover is
perennial herbaceous
vegetation
37
Table A4. Taxa, ID number (crayfish id), and rarity of crayfish found in study area.
(Rarity is the number of watersheds in which the taxa was found out of a total of 991.)
Crayfish – Scientific Name
ID
Genus
Species
Subspecies/
#
Rarity
“Comment”
Cambarus
acanthura
1
44
Cambarus
aculabrum
2
5
Cambarus
acuminatus
3
60
Cambarus
angularis
4
12
Cambarus
asperimanus
5
50
Cambarus
attiguus
6
1
Cambarus
bartonii
bartonii
7
188
Cambarus
bartonii
cavatus
8
44
Cambarus
batchi
9
2
Cambarus
bouchardi
10
1
Cambarus
buntingi
11
11
Cambarus
carinirostris
12
15
Cambarus
carolinus
13
27
Cambarus
catagius
14
1
Cambarus
causeyi
15
5
Cambarus
chasmodactylus
16
22
Cambarus
chaugaensis
17
8
Cambarus
conasaugaensus
18
8
Cambarus
coosae
19
20
Cambarus
coosawattae
20
4
Cambarus
cornutus
21
7
Cambarus
crinipes
22
3
Cambarus
cumberlandensis
23
23
Cambarus
cymatilis
24
2
Cambarus
deweesae
25
2
Cambarus
diogenes
26
265
Cambarus
distans
27
31
Cambarus
dubius
28
75
Cambarus
elkensis
29
13
Cambarus
englishi
30
5
Cambarus
extraneus
31
2
Cambarus
fasciatus
32
2
Cambarus
friaufi
33
2
Cambarus
gentryi
34
1
Cambarus
georgiae
35
15
Cambarus
girardianus
36
9
Cambarus
graysoni
37
5
Cambarus
halli
38
9
Cambarus
hiwaseensis
39
13
Cambarus
hobbsorum
40
7
38
Table A4. continued.
Crayfish – Scientific Name
Genus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarus
Cambarellus
Species
howardii
hubbsi
hubrichti
jezerinaci
laevis
latimanus
longirostris
longulus
ludovicianus
manningi
monongalensis
nerterius
nodosus
obstipus
ortmanni
parrishi
paravoculus
pyronotus
reburrus
reduncus
reflexus
robustus
rusticiformis
sciotensis
scotti
setosus
speciosus
sp.
sphenoides
spicatus
sp.
striatus
tenebrosus
thomai
tuckesegee
veteranus
zophonastes
diminutus
lesliei
puer
Subspecies/
“Comments”
“Freckled crayfish”
“Ohio crayfish”
ID
#
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
Rarity
26
34
18
1
11
84
53
23
25
12
16
4
13
6
5
10
15
1
23
12
1
99
10
25
7
5
1
7
9
2
8
70
18
39
1
15
3
5
3
41
39
Table A4. continued.
Crayfish – Scientific Name
Genus
Cambarellus
Cambarellus
Distocambarus
Distocambarus
Distocambarus
Fallicambarus
Fallicambarus
Fallicambarus
Fallicambarus
Fallicambarus
Fallicambarus
Fallicambarus
Fallicambarus
Fallicambarus
Fallicambarus
Fallicambarus
Fallicambarus
Fallicambarus
Faxonella
Faxonella
Faxonella
Faxonella
Hobbseus
Hobbseus
Hobbseus
Hobbseus
Hobbseus
Hobbseus
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Species
schmitti
shufeldtii
carlsoni
crockeri
youngineri
burrisi
byersi
caesius
danielae
devastator
fodiens
gordoni
harpi
hedgpethi
jeanae
orkytes
strawni
uhleri
beyeri
blairi
clypeata
creaseri
attenuatus
petilus
oronectoides
prominens
valleculus
yalobushensis
acares
alabamensis
australis
australis
bisectus
blacki
burri
carolinensis
chickasawae
compressus
cristavarius
difficilis
Subspecies/
“Comments”
australis
packardi
ID
#
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
Rarity
2
31
2
6
1
7
11
4
6
11
47
8
15
47
6
9
2
9
10
7
39
14
1
2
1
8
3
1
15
1
3
3
25
5
1
3
17
6
75
17
40
Table A4. continued.
Crayfish – Scientific Name
Genus
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Species
durelli
erichsonianus
etnieri
eupunctus
forceps
hathawayi
harrisoni
hartfieldi
holti
hylas
illinoiensis
immunis
indianensis
inermis
inermis
jonesi
kentuckiensis
lancifer
leptogonopodus
limosus
longidigitus
luteus
macrus
maletae
medius
meeki
meeki
menae
mississippiensis
nais
nana
neglectus
neglectus
obscurus
ozarkae
palmeri
palmeri
palmeri
pellucidus
perfectus
Subspecies/
“Comments”
inermis
testii
brevis
meeki
chaenodactylus
neglectus
creolanus
longimanus
palmeri
ID
#
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
Rarity
2
34
6
3
31
5
2
20
5
39
12
157
6
3
2
14
3
15
27
8
36
56
31
3
55
48
50
32
1
17
30
24
28
36
42
3
120
37
2
2
41
Table A4. continued.
Crayfish – Scientific Name
Genus
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Orconectes
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Species
peruncus
placidus
propinquus
punctumanus
putnami
quadruncus
rafinesquei
rusticus
sanbornii
saxatilis
sloanii
spinosus
sp.
tricuspis
validus
virilis
williamsi
ablusus
acutissimus
acutus
alleni
ancylus
barbiger
blandingii
capillatus
clarkii
clemmeri
cometes
curdi
delicatus
dupratzi
elegans
enoplosternum
escambiensis
evermanni
fallax
fitzpatricki
geminus
geodytes
gracilis
Subspecies/
“Comments”
“Arkansas”
acutus
ID
#
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
Rarity
28
23
99
115
8
5
1
178
28
2
2
43
4
2
14
270
17
2
24
190
1
2
9
4
2
58
5
1
12
1
25
8
4
1
6
19
9
2
5
30
42
Table A4. continued.
Crayfish – Scientific Name
Genus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Species
hagenianus
hagenianus
hayi
hinei
hubbelli
hybus
jaculus
kensleyi
kilbyi
latipleurum
lecontei
leonensis
lewisi
liberorum
lophotus
lucifugus
lylei
mancus
marthae
nechesae
nigrocincatus
natchitochae
okaloosae
ouachitae
paeninsulanus
parasimulans
pecki
penni
perfectus
planirostris
plumimanus
pogum
pubischelae
pycnogonopodus
pygmaeus
raneyi
rathbunae
regalis
reimeri
rogersi
Subspecies/
”Comments”
hagenianus
vesticeps
lucifugus
pubischelae
campestris
ID
#
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
Rarity
1
3
24
11
4
12
12
23
16
2
7
15
2
45
16
2
1
22
9
5
11
23
8
12
28
26
1
11
4
13
6
4
9
2
12
7
3
1
12
5
43
Table A4. continued.
Crayfish – Scientific Name
Genus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Procambarus
Species
rogersi
seminolae
shermani
simulans
spiculifer
tenuis
troglodytes
tulanei
verrucosus
versutus
viaeviridus
vioseai
youngi
zonangulus
Subspecies/
“Comments”
ochlockensis
simulans
ID
#
241
242
243
244
245
246
247
248
249
250
251
252
253
254
Rarity
5
9
2
51
45
10
15
21
10
23
15
40
1
2
44
Table A5. Crayfish distribution, RWRI_BC, total number of taxa (given in parentheses)
by watershed, state, and national forest. (see Table A4 for crayfish taxa codes) (see Table
A6 for national forest codes)
WS
State
National
Crayfish
RWRI_BC
Forest
ID
(total # taxa)
0001 NH
WMF
168
0.460 (1)
0002 NH
WMF
168
0.460 (1)
0003 ME
WMF
168
0.460 (1)
0004 ME
WMF
132,140,168
0.742 (3)
0005 NH
WMF
140,168
0.737 (2)
0006 NH
WMF
140,168
0.737 (2)
0007 NH
WMF
7,132,140,168,176
0.749 (5)
0008 NH
WMF
140,168
0.737 (2)
0009 NH
WMF
7,132,140,168,176
0.749 (5)
0010 NH
WMF
168
0.460 (1)
0011 NH
WMF
7,168,176
0.531 (3)
0012 NH
WMF
7,140,168,176
0.744 (4)
0013 NH
WMF
7,140,168,176
0.744 (4)
0014 NH
WMF
168
0.460 (1)
0015 NH
WMF
168
0.460 (1)
0016 NH
WMF
168
0.460 (1)
0017 NH
WMF
168
0.460 (1)
0018 NH
WMF
168
0.460 (1)
0019 NH
WMF
7,168,176
0.531 (3)
0020 NH
WMF
7,168,176
0.531 (3)
0022 NH
WMF
7,168,176
0.531 (3)
0023 VT
GMF
163,168
0.536 (2)
0025 VT
GMF
163,168
0.536 (2)
0026 VT
GMF
7,168
0.508 (2)
0028 VT
GMF
7,163,168,176
0.574 (4)
0030 VT
GMF
7,163,168,176
0.574 (4)
0031 VT
GMF
163,168
0.536 (2)
0032 VT
GMF
163,168
0.536 (2)
0033 VT
GMF
163,168
0.536 (2)
0034 VT
GMF
168
0.460 (1)
0035 VT
GMF
168
0.460 (1)
0036 VT
GMF
163,168
0.536 (2)
0038 VT
GMF
163,168
0.536 (2)
0040 VT
GMF
163,168
0.536 (2)
0041 VT
GMF
168
0.460 (1)
0042 VT
GMF
168
0.460 (1)
0043 VA
WJF
7,51,154
0.703 (3)
0044 VA
WJF
7,51,154
0.703 (3)
0045 WV
MON
7
0.456 (1)
0046 WV
MON
7
0.456 (1)
45
Table A5.
WS
ID
0047
0048
0049
0050
0051
0053
0054
0055
0056
0057
0058
0059
0060
0061
0062
0063
0064
0065
0066
0067
0068
0069
0070
0071
0072
0073
0074
0075
0076
0077
0078
0079
0080
0081
0082
0083
0084
0085
0086
0087
0088
0089
0090
continued.
State
National
Forest
WV
MON
VA
WJF
WV
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
NC
NFN
NC
NFN
NC
NFN
NC
NFN
NC
NFN
NC
NFN
NC
NFN
NC
NFN
NC
NFN
Crayfish
7,51
7,51,154
7
7
7
7
7
7
7,154
7,154
7,154
7,154
3,7,48,172,176
3,7,48,172,176
3,7,48,172
3,7,48,176
3,7,48,172
3,7,48
3,7,48,172,176
3,7,48,172,176
3,7,48,172,176
3,7,48,176
3,7,48,176
3,7,48,172,176
3,7,48,172,176
3,7,48,172,176
3,7,48
3,48
3,7,48
3,48
3,7,48,172
3,7,48,172
3,7,48,172
3,7,48,172
3,26,46,91,180,231
3,26,46,91,180,186,231
3,26,46,91,116,180,231
3,26,46,91,180,231
3,26,46,91,116,180,186,231
3,26,46,91,116,180,186,231
3,40,60,180
3,5,28,41,48
3,14,40,41,60,180
RWRI_BC
(total # taxa)
0.668 (2)
0.703 (3)
0.456 (1)
0.456 (1)
0.456 (1)
0.456 (1)
0.456 (1)
0.456 (1)
0.600 (2)
0.600 (2)
0.600 (2)
0.600 (2)
0.700 (5)
0.700 (5)
0.695 (4)
0.670 (4)
0.695 (4)
0.664 (3)
0.700 (5)
0.700 (5)
0.700 (5)
0.670 (4)
0.670 (4)
0.700 (5)
0.700 (5)
0.700 (5)
0.664 (3)
0.656 (2)
0.664 (3)
0.656 (2)
0.695 (4)
0.695 (4)
0.695 (4)
0.695 (4)
0.800 (6)
0.809 (7)
0.916 (7)
0.800 (6)
0.921 (8)
0.921 (8)
0.811 (4)
0.738 (5)
1.039 (6)
46
Table A5.
WS
ID
0091
0092
0093
0094
0095
0096
0097
0098
0099
0101
0102
0103
0104
0105
0106
0107
0108
0109
0110
0111
0112
0113
0114
0115
0116
0117
0118
0119
0121
0122
0123
0124
0125
0126
0127
0128
0129
0130
0131
0132
0133
0134
0135
continued.
State
National
Forest
NC
NFN
NC
NFN
NC
NFN
NC
NFN
NC
NFN
NC
NFN
NC
NFN
NC
NFN
NC
NFN
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
NC
NFN
NC
FMS
SC
FMS
SC
FMS
NC
FMS
GA
CHA
SC
FMS
SC
FMS
SC
FMS
SC
FMS
GA
CHA
SC
FMS
SC
FMS
SC
FMS
SC
FMS
GA
CHA
GA
CHA
GA
CHA
GA
CHA
GA
CHA
GA
CHA
Crayfish
3,40,41,60,180,186
3,40,41,60,180
3,40,41,60,180
3,40,41,60,180,186
3,5,7,28,41,176
3,5,7,28,41,176
3,5,7,28,41
3,5,7,28,41
3,5,7,28,41
3,46
3,46
46,60
3,46,60,72,180
3,46,60,70,72,180,247
3,7,46,70
3,46
46,60,72
3,41,46,72
3,46
3,46
3,46,72
46,61,72,84,247
5,7,17,46,59
5,7,17,46,59,245
5,7,17,46,47,72,236
5,17,46,236,245
5,7,17,39,46,53,59,236,245
5,7,17,53
5,7,17,53,236,245
7,46,72,84,247
7,41,46,83,84,236,245
46,65,236,247
7,46,236
72
46,60,72,247
26,46,72,84,247
46,84
7,245
7
245
46,245
46,245
245
RWRI_BC
(total # taxa)
0.836 (6)
0.829 (5)
0.829 (5)
0.836 (6)
0.705 (6)
0.705 (6)
0.701 (5)
0.701 (5)
0.701 (5)
0.587 (2)
0.587 (2)
0.703 (2)
0.738 (5)
0.948 (7)
0.910 (4)
0.587 (2)
0.718 (3)
0.686 (4)
0.587 (2)
0.587 (2)
0.623 (3)
1.035 (5)
0.789 (5)
0.801 (6)
0.850 (7)
0.844 (5)
0.908 (9)
0.801 (4)
0.869 (6)
0.819 (5)
0.982 (7)
0.859 (4)
0.760 (3)
0.529 (1)
0.771 (4)
0.819 (5)
0.772 (2)
0.583 (2)
0.456 (1)
0.565 (1)
0.603 (2)
0.603 (2)
0.565 (1)
47
Table A5.
WS
ID
0136
0137
0138
0139
0140
0141
0142
0143
0144
0145
0146
0147
0148
0149
0150
0151
0152
0153
0154
0155
0156
0157
0158
0159
0160
0161
0162
0163
0164
0165
0166
0167
0168
0169
0170
0171
0172
0173
0174
0175
0176
continued.
State
National
Forest
GA
CHA
GA
CHA
GA
CHA
GA
CHA
GA
CHA
GA
CHA
GA
CHA
GA
CHA
GA
CHA
AL
NFA
AL
NFA
AL
NFA
AL
NFA
FL
NFA
FL
NFA
AL
NFA
AL
NFA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
AL
AL
AL
AL
AL
AL
AL
CHA
CHA
CHA
CHA
CHA
CHA
CHA
CHA
CHA
CHA
CHA
CHA
CHA
CHA
CHA
CHA
CHA
NFA
NFA
NFA
NFA
NFA
NFA
NFA
Crayfish
46
46,245
46,245
46,72,245
46
7,56,245
7,245
7,53,245
7,46,245
46,87,179,205,223,245,249,250
46,87,179,205,223,245,249,250
46,87,179,205,223,249,250
46,87,179,205,223,237,245,249,250
46,87,179,195,223,237,249,250
46,87,179,195,223,237,249,250
46,87,179,185,223,243,249,250
46,87,179,185,194,195,223,243,245,
249,250
1,7,18,19,24,26,72,172
1,7,19,24,41,50,72,172,245
1,19,26,36,50,72,125,172
1,18,46,72,172,245
1,19,72,172,215,245
18,20,46,67
18,20,46
18,20,46
1,18,20,46
1,19,46,72,172,215,245
172,215
1,19,46
1,19,39,46,50,72,122,245
32,46,245
19,32,46,245
1,46,47,65,122,215
1,19,46,50,65,122,172,215
1,19,46,50,65,72,122,172,245
1,19,36,46,50,65,72,122,172,245
1,19,36,47,50,65,72,122,172
1,19,36,46,50,65,72,122,172
1,19,38,50,122,172
1,19,46,50,72,122,172,250
1,19,36,46,50,122,172,250
RWRI_BC
(total # taxa)
0.514 (1)
0.603 (2)
0.603 (2)
0.635 (3)
0.514 (1)
0.734 (3)
0.583 (2)
0.713 (3)
0.616 (3)
0.945 (8)
0.945 (8)
0.940 (7)
1.003 (9)
0.986 (8)
0.986 (8)
1.053 (8)
1.212 (11)
0.957 (8)
0.960 (9)
0.851 (8)
0.796 (6)
0.783 (6)
1.050 (4)
0.867 (3)
0.867 (3)
0.875 (4)
0.790 (7)
0.692 (2)
0.690 (3)
0.839 (8)
0.910 (3)
0.923 (4)
0.830 (6)
0.880 (8)
0.872 (9)
0.904 (10)
0.900 (9)
0.898 (9)
0.843 (6)
0.825 (8)
0.863 (8)
48
Table A5. continued.
WS State
National
Forest
ID
0177 AL
NFA
0178 AL
NFA
0179 AL
NFA
0180 AL
NFA
0181 AL
NFA
0182 AL
NFA
0183 AL
NFA
0184
0185
AL
AL
NFA
NFA
0186
AL
NFA
0187
0188
AL
AL
NFA
NFA
0189
MS
NFM
0190
MS
NFM
0191
MS
NFM
0192
MS
NFM
0193
0194
0195
0196
0197
0198
0199
0200
AL
AL
AL
AL
AL
AL
AL
AL
NFA
NFA
NFA
NFA
NFA
NFA
NFA
NFA
0201
AL
NFA
0202
AL
NFA
0203
AL
NFA
0204
0205
0206
MS
MS
MS
NFM
NFM
NFM
Crayfish
1,19,36,46,50,72,122,172,250
30,38
30,38
30,38,46,72,245
30,38,46,245
26,38,46,72,101,179,186,215,249,250
26,30,38,46,72,101,179,186,213,249,
250
26,72,129,160,206,213,215,219,250
1,19,26,36,38,72,106,122,129,179,
219,250
1,19,26,36,38,46,72,106,122,129,179,
219,245,250
1,72,106,129,179,219,250
1,19,26,72,106,122,129,179,215,219,
250
49,72,91,104,117,175,179,202,203,
206,218,229,232
72,104,117,176,179,180,202,203,206,
218,229,232
72,117,176,179,180,202,203,206,218,
229,232
7,26,46,72,94,105,107,117,136,149,
179,180,188,201,203,206,218,229,
232,252,254
1,54,72,175
1,72,175
1,54,72,175
1,72,175
1,72,175
1,72,175
72,175
1,26,54,72,106,110,179,180,206,215,
219,250
1,26,54,72,106,179,180,206,215,219,
250
1,26,54,72,106,179,180,206,215,219,
250
1,26,54,72,106,179,180,206,215,219,
250
136,218,230
136,218,228,230
26,136,156,180,183,218,252
RWRI_BC
(total # taxa)
0.868 (9)
0.839 (2)
0.839 (2)
0.858 (5)
0.853 (4)
0.882 (10)
1.010 (11)
1.065 (9)
0.978 (12)
0.984 (14)
0.916 (7)
0.949 (11)
1.091 (13)
1.076 (12)
1.018 (11)
1.300 (21)
0.824 (4)
0.717 (3)
0.824 (4)
0.717 (3)
0.717 (3)
0.717 (3)
0.692 (2)
1.081 (12)
0.944 (11)
0.944 (11)
0.944 (11)
0.782 (3)
0.828 (4)
0.925 (7)
49
Table A5.
WS
ID
0207
0208
0209
0210
0211
0212
0213
0214
0215
0216
continued.
State
National
Forest
MS
NFM
MS
NFM
MS
NFM
MS
NFM
MS
NFM
MS
NFM
MS
NFM
MS
NFM
MS
NFM
MS
NFM
0217
MS
NFM
0218
0219
0220
MS
MS
MS
NFM
NFM
NFM
0221
0222
MS
MS
NFM
NFM
0224
MS
NFM
0225
MS
NFM
0226
0227
0228
0229
0230
0231
0232
0233
0234
0235
0236
0237
0238
0239
0240
0241
0242
0243
0245
MS
MS
MS
MS
MS
MS
MS
MS
MS
MS
MN
MN
MN
MN
MN
MN
MN
MN
MN
NFM
NFM
NFM
NFM
NFM
NFM
NFM
NFM
NFM
NFM
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
Crayfish
26,136,156,180,183,218,252
26,49,136,156,180,218
49,92,96,136,218,230
92,136,218
26,91,126,136,218,228,230,252
26,91,126,136,218,228,230,252
136,218
92,136,218,230,252
136,218
26,78,79,82,86,92,136,187,197,211,
218,228,230
78,82,86,87,89,96,101,138,186,195,
197,203,211,228
26,49,86,89,92,96,187,197,218,230
49,86,89,92,96,197,218,228,230
78,79,82,86,89,92,96,180,187,197,
211,218,228,230
49,86,96,197,211,218,228
78,79,82,86,89,92,96,187,197,203,
211,218,228,230
26,78,82,87,89,96,101,180,186,195,
197,211,228,230,252
26,78,82,87,89,96,101,180,186,195,
197,211,228,230,252
103,107
183,207
49,183,207
49,183,207
49,183,207
72,107
49,183,192
26,49,72,94,183,192,207,254
158,180,183,192,207,252
26,180,192,207,251
26,132,163,176
26,132,163,176
26,132,163,176
26,132,163,176
26,132,163,176
26,132,163,176
26,132,163,176
26,132,163,176
26,132,163,176
RWRI_BC
(total # taxa)
0.925 (7)
0.909 (6)
0.902 (6)
0.808 (3)
0.910 (8)
0.910 (8)
0.725 (2)
0.852 (5)
0.725 (2)
1.071 (13)
1.053 (14)
1.008 (10)
0.991 (9)
1.091 (14)
0.951 (7)
1.094 (14)
1.016 (15)
1.036 (15)
1.044 (2)
0.782 (2)
0.820 (3)
0.820 (3)
0.820 (3)
0.853 (2)
0.838 (3)
0.989 (8)
0.864 (6)
0.828 (5)
0.571 (4)
0.571 (4)
0.571 (4)
0.571 (4)
0.571 (4)
0.571 (4)
0.571 (4)
0.571 (4)
0.571 (4)
50
Table A5.
WS
ID
0246
0247
0248
0249
0250
0251
0252
0253
0254
0255
0256
0257
0259
0260
0261
0262
0263
0264
0265
0266
0267
0268
0269
0270
0271
0272
0273
0274
0275
0276
0277
0278
0279
0280
0283
0284
0285
0286
0287
0288
0289
0290
0291
continued.
State
National
Forest
MN
SUP
MN
SUP
MN
SUP
MN
SUP
MN
SUP
MN
SUP
MN
SUP
MN
SUP
MN
SUP
MN
SUP
MN
SUP
MI
OTT
MI
OTT
MI
OTT
MI
OTT
MI
OTT
MI
OTT
MI
OTT
MI
OTT
MI
OTT
MI
OTT
MI
OTT
MI
OTT
MI
OTT
MI
HIA
MI
HIA
MI
HIA
MI
HIA
MI
OTT
MI
OTT
WI
OTT
MI
OTT
MI
HIA
MI
HIA
MI
HIA
MI
HIA
MI
HMF
MI
HMF
MI
HMF
MI
HMF
MI
HMF
MI
HMF
MI
HMF
Crayfish
26,132,163,176
26,132,163,176
26,132,163,176
26,132,176
26,132,176
26,132,176
26,132,176
26,132,176
26,132,176
26,132,176
26,132,176
168,176
26,168,176
26,168,176
168,176
168,176
168,176
168,176
168,176
168,176
168,176
168,176
26,163,168,176
26,163,168,176
163,176
163,176
163,176
163,176
176
168,176
168
168
168
168
168
168
26,62,163,168,176
132,163,168,176
26,132,163,168,176
26,132,163,168,176
132,163,168,176
26,132,163,168,176
26,132,163,168,176
RWRI_BC
(total # taxa)
0.571 (4)
0.571 (4)
0.571 (4)
0.526 (3)
0.526 (3)
0.526 (3)
0.526 (3)
0.526 (3)
0.526 (3)
0.526 (3)
0.526 (3)
0.496 (2)
0.522 (3)
0.522 (3)
0.496 (2)
0.496 (2)
0.496 (2)
0.496 (2)
0.496 (2)
0.496 (2)
0.496 (2)
0.496 (2)
0.569 (4)
0.569 (4)
0.526 (2)
0.526 (2)
0.526 (2)
0.526 (2)
0.432 (1)
0.496 (2)
0.460 (1)
0.460 (1)
0.460 (1)
0.460 (1)
0.460 (1)
0.460 (1)
0.600 (5)
0.578 (4)
0.590 (5)
0.590 (5)
0.578 (4)
0.590 (5)
0.590 (5)
51
Table A5.
WS
ID
0292
0293
0294
0295
0296
0297
0298
0299
0300
0301
0302
0303
0304
0305
0306
0307
0308
0309
0311
0313
0314
0315
0316
0317
0318
0319
0321
0322
0323
0324
0325
0326
0327
0328
0329
0330
0331
0332
0333
0334
0335
0336
0337
continued.
State
National
Forest
MI
HMF
MI
HMF
MI
HMF
MI
HMF
MI
HMF
MI
HMF
MI
HMF
MI
HMF
MI
HMF
MI
HMF
MI
HMF
MI
HIA
MI
HIA
MI
HIA
MI
HIA
MI
HIA
MI
HIA
MI
HIA
MI
HMF
MI
HMF
MI
HMF
MI
HMF
MI
HMF
MI
HMF
MI
HMF
MI
HMF
MI
HMF
MI
HMF
MI
HMF
MI
HMF
PA
ALL
PA
ALL
PA
ALL
PA
ALL
PA
ALL
PA
ALL
PA
ALL
PA
ALL
PA
ALL
PA
ALL
PA
ALL
PA
ALL
PA
ALL
Crayfish
26,132,163,168,176
163,168,176
163,168,176
163,168,176
163,168,176
163,168
26,163,168,176
168
163,168
163,168,176
163,168,176
168
168
176
163,176
176
176
176
132,163,168,176
62,163,168,176
163,168,176
62,132,163,168,176
62,163,168,176
62,132,163,168,176
62,163,168,176
163,168,176
62,163,168,176
168,176
163,168,176
163,168
7,12,62,154
7,12,62,154
7,12,62,154
7,12,62,154
7,12,62,154
7,12,62,154
7,12,154
7,12,154
7,12,154
7,12,154
7,12,154
7,12,154
7,12,154
RWRI_BC
(total # taxa)
0.590 (5)
0.554 (3)
0.554 (3)
0.554 (3)
0.554 (3)
0.536 (2)
0.569 (4)
0.460 (1)
0.536 (2)
0.554 (3)
0.554 (3)
0.460 (1)
0.460 (1)
0.432 (1)
0.526 (2)
0.432 (1)
0.432 (1)
0.432 (1)
0.578 (4)
0.590 (4)
0.554 (3)
0.607 (5)
0.590 (4)
0.607 (5)
0.590 (4)
0.554 (3)
0.590 (4)
0.496 (2)
0.554 (3)
0.536 (2)
0.718 (4)
0.718 (4)
0.718 (4)
0.718 (4)
0.718 (4)
0.718 (4)
0.708 (3)
0.708 (3)
0.708 (3)
0.708 (3)
0.708 (3)
0.708 (3)
0.708 (3)
52
Table A5.
WS
ID
0338
0339
0340
0341
0342
0343
0344
0345
0346
0347
0348
0349
0350
0351
0352
0357
0358
0359
0360
0361
0362
0363
0364
0365
0366
0367
0368
0369
0370
0371
0372
0373
0374
0375
0376
0377
0378
0379
0380
0381
0382
0383
0384
continued.
State
National
Forest
PA
ALL
PA
ALL
WV
MON
WV
MON
WV
MON
WV
MON
WV
MON
WV
MON
WV
MON
WV
MON
WV
MON
WV
MON
WV
MON
WV
MON
OH
WAY
OH
WAY
OH
WAY
OH
WAY
OH
WAY
OH
WAY
OH
WAY
NC
WJF
NC
CHR
NC
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
VA
WJF
WV
WJF
WV
WJF
WV
MON
WV
MON
WV
MON
WV
MON
WV
MON
WV
MON
WV
MON
WV
MON
Crayfish
7,12,154
7,12,154
8
8,154
8,154
8,51
8
8,51
8,51
8,51,154
8,154
8,51,154
8
8,28
169
168,169
74,169
74,169
168,169
169
169
7,16,28,41,62,64,76,119
7,16,28,41,62,64,76,119
7,16,28,41,62,64,76,119
7,16,62,64,76,119
3,7,16,28,62,64,76,119
7,16,28,62,64,76
7,16,28,62,64,76
3,16,62,64,119,176
3,7,16,28,64,76,119,176
3,7,16,28,62,64,76,119,176
3,7,16,28,62,64,76,119,168,176
3,7,16,28,64,76,119,176
3,7,8,16,28,64,76,119,172,176
8,62,64,172,176
8,16,28,62,154
8,16,28,51,62,154
8,16,28,51,62,64,154
8,16,51,64,154
8,16,28,52,154,172
8,16,28,52,62,64,154
8,16,52,154
8,16,52,62,172
RWRI_BC
(total # taxa)
0.708 (3)
0.708 (3)
0.567 (1)
0.639 (2)
0.639 (2)
0.691 (2)
0.567 (1)
0.691 (2)
0.691 (2)
0.721 (3)
0.639 (2)
0.721 (3)
0.567 (1)
0.608 (2)
0.607 (1)
0.620 (2)
0.658 (2)
0.658 (2)
0.620 (2)
0.607 (1)
0.607 (1)
0.804 (8)
0.804 (8)
0.804 (8)
0.774 (6)
0.792 (8)
0.774 (6)
0.774 (6)
0.736 (6)
0.788 (8)
0.794 (9)
0.797 (10)
0.788 (8)
0.812 (10)
0.708 (5)
0.727 (5)
0.774 (6)
0.798 (7)
0.785 (5)
0.866 (6)
0.875 (7)
0.853 (4)
0.855 (5)
53
Table A5.
WS
ID
0385
0386
0387
0388
0389
0390
0391
0392
0393
0396
0406
0407
0408
0409
0410
0411
0412
0413
0414
0415
0416
0417
0418
0419
0420
0421
0422
0423
0424
0425
0426
0427
0428
0429
0430
0431
0432
0433
0434
0435
0436
0437
0438
continued.
State
National
Forest
WV
MON
WV
MON
WV
MON
WV
MON
WV
MON
WV
MON
KY
WJF
VA
WJF
KY
WJF
OH
WAY
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
IN
HOO
IN
HOO
IN
HOO
IN
HOO
IN
HOO
IN
HOO
IN
HOO
IN
HOO
Crayfish
8,51,64,172
8,51,64,172
8,51,64,172
8,28,51,64,154
8,28,64,154
29
8,76,119
8,57,76,119
8,76,119
74
55,74,113,119,165,168,180
113,119
113,119,165,180
7,55,74,113,119
8,28,62,113,119,168
62,119,168
119
7,27,57,62,113,119
7,27,57,62,113,119
11,27,57,62,113,119
23,27,28,57,62,113,119
27,62,69,119
113,119
27,62,119
62,113,119
7,27,62,119
7,27,62,69,113,119
9,27,55,62,113,119
27,62,113,119
7,62,73,113,119
119
62,168
7,8,55,57,62,113,119,168
7,28,62,113,119
62,72,119
168
45,168
91,163,168
91,163,168
26,45,91,135,163,168
26,45,91,135,163,168
45,168
45,168
RWRI_BC
(total # taxa)
0.751 (4)
0.751 (4)
0.751 (4)
0.764 (5)
0.712 (4)
0.681 (1)
0.711 (3)
0.766 (4)
0.711 (3)
0.577 (1)
0.876 (7)
0.644 (2)
0.775 (4)
0.828 (5)
0.713 (6)
0.588 (3)
0.523 (1)
0.765 (6)
0.765 (6)
0.814 (6)
0.796 (7)
0.764 (4)
0.644 (2)
0.648 (3)
0.661 (3)
0.657 (4)
0.792 (6)
0.966 (6)
0.703 (4)
0.731 (5)
0.523 (1)
0.536 (2)
0.859 (8)
0.687 (5)
0.612 (3)
0.460 (1)
0.704 (2)
0.610 (3)
0.610 (3)
0.933 (6)
0.933 (6)
0.704 (2)
0.704 (2)
54
Table A5.
WS
ID
0439
0440
0441
0442
0443
0444
0445
0446
0447
0448
0449
0450
0451
0452
0453
0454
0455
0456
0457
0458
0459
0460
0461
0462
0463
0464
0465
0466
0467
continued.
State
National
Forest
IN
HOO
IN
HOO
IN
HOO
IN
HOO
IN
HOO
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
KY
DBF
0468
KY
DBF
0469
TN
LBL
0471
0472
0473
0474
0475
0476
0478
0479
0480
KY
IN
IN
IN
IN
IN
IN
IN
IN
LBL
HOO
HOO
HOO
HOO
HOO
HOO
HOO
HOO
Crayfish
168
45,168
45,168
26,45,133,168
26,45,133,168
7,11,23,27,28,57,113,119
11,26,27,28,57,69,74,119,168
25,27,74,119
21,23,27,63,119,168
11,23,26,27,28,57,63,69,74,119
11,23,26,27,28,57,69,74,113,119
21,23,27,28,119
21,23,27,119
23,27
23,27,28,113,119
7,9,23,63,113,119
7,21,23,27,28,63,113,119,162,165
7,23,63,69,119,162,165
7,21,23,27,55,63,73,111,113,119,162
21,23,27,28,37,63,113,119,162,165
23,63,73,119,162
21,23,27,119
23,33
25,28,73,112
23,27,119
23,31,73,162
23,27,28,73,113,119,159,165
10,11,22,23,26,27,28,57,69,74
22,23,26,27,28,37,57,69,74,111,112,
159,162
22,23,26,27,28,37,57,63,69,73,74,
111,112,162,165
26,33,34,37,72,73,91,118,121,162,
175,180
26,63,73,118,162,174
26,45,134,165,168,171
26,134,168,171
134,168
168
168
168
168
26,133,168
RWRI_BC
(total # taxa)
0.460 (1)
0.704 (2)
0.704 (2)
0.820 (4)
0.820 (4)
0.837 (8)
0.859 (9)
0.919 (4)
0.850 (6)
0.901 (10)
0.884 (10)
0.810 (5)
0.803 (4)
0.679 (2)
0.746 (5)
0.948 (6)
0.916 (10)
0.885 (7)
1.001 (11)
0.958 (10)
0.815 (5)
0.803 (4)
0.913 (2)
0.985 (4)
0.696 (3)
0.936 (4)
0.971 (8)
1.085 (10)
1.113 (13)
1.094 (15)
1.153 (12)
0.979 (6)
1.009 (6)
0.975 (4)
0.850 (2)
0.460 (1)
0.460 (1)
0.460 (1)
0.460 (1)
0.771 (3)
55
Table A5.
WS
ID
0481
0482
0485
0486
0487
0488
0492
0499
0500
0501
0502
0503
0504
0505
0506
0507
0508
0509
0510
0511
0512
0513
0514
0515
0516
0517
0518
0519
0520
0521
0522
0523
0524
0525
0526
0527
0528
0529
0530
0531
0533
0534
0535
continued.
State
National
Forest
IL
SHA
IL
SHA
IL
SHA
IL
SHA
IL
SHA
IL
SHA
IL
SHA
VA
WJF
VA
WJF
VA
WJF
VA
WJF
TN
CHR
TN
CHR
TN
CHR
TN
CHR
TN
CHR
TN
CHR
NC
CHR
NC
NFN
NC
NFN
NC
NFN
TN
CHR
NC
NFN
TN
CHR
NC
NFN
NC
CHR
NC
NFN
NC
NFN
NC
NFN
TN
CHR
NC
NFN
NC
CHR
NC
NFN
NC
NFN
NC
NFN
NC
NFN
TN
CHR
TN
CHR
TN
CHR
NC
NFN
NC
NFN
NC
NFN
NC
NFN
Crayfish
73,131,132,133,137,162
73,91,131,132,133,137,162
26,73,91,131,132
26,73,91,131,132,180
26,91,131,132,180,186,251
26,180
26,137,162,163,180,200
4,7,8,11,29,62,119,125,180
4,7,8,11,26,29,47,62,119,125,168,180
4,7,8,11,29,62,119,125,180
4,7,8,26,29,47,62,74,119,122,125,168
5,7,47,62,119,125
5,7,28,47,62,119,125
5,7,28,47,62,125
5,7,26,47,62,125
5,7,26,47,62,125
5,7,26,47,62,74,122,125
5,7,28,41,59,62
5,7,28,41,59,62
5,7,28,41,59,62
5,7,28,41,59,62
5,7,13,26,47,62,74,122,125,176
5,7,28,41,59,62
5,7,13,47,62,125,176
5,7,28,41,59,62
7,28,47,59,62
7,28,47,59,62
7,28,47,59,62
7,28,47,59,62
5,7,13,26,47,62,74,122,125,176
5,7,28,62
5,7,28,62
5,7,28,62
5,7,28,47,62
5,7,28,47,62
5,7,28,47,62
5,7,26,28,47,62,74,122,125,168
5,7,28,47,62,125,168
5,7,26,28,47,62,74,122,125,168,176
5,7,26,28,47,62,74,122,125,168,176
7,28,47,62
5,7,28,47,62,125
7,28,47,62
RWRI_BC
(total # taxa)
0.946 (6)
0.950 (7)
0.767 (5)
0.770 (6)
0.788 (7)
0.494 (2)
0.881 (6)
0.851 (9)
0.861 (12)
0.851 (9)
0.846 (12)
0.708 (6)
0.721 (7)
0.708 (6)
0.697 (6)
0.697 (6)
0.749 (8)
0.737 (6)
0.737 (6)
0.737 (6)
0.737 (6)
0.777 (10)
0.737 (6)
0.734 (7)
0.737 (6)
0.698 (5)
0.698 (5)
0.698 (5)
0.698 (5)
0.777 (10)
0.636 (4)
0.636 (4)
0.636 (4)
0.668 (5)
0.668 (5)
0.668 (5)
0.763 (10)
0.714 (7)
0.765 (11)
0.765 (11)
0.633 (4)
0.708 (6)
0.633 (4)
56
Table A5.
WS
ID
0536
0537
0538
0539
0540
0541
0542
0543
0544
0545
0546
0547
0548
0549
0550
0551
0552
continued.
State
National
Forest
NC
NFN
NC
CHA
NC
NFN
NC
NFN
NC
NFN
NC
NFN
NC
NFN
NC
NFN
NC
NFN
NC
NFN
NC
NFN
NC
NFN
NC
NFN
NC
NFN
TN
CHR
VA
WJF
VA
WJF
0553
0554
0555
0556
VA
GA
GA
TN
WJF
CHA
CHA
CHR
0557
0558
0559
0560
0561
0562
0563
0564
0565
0566
0567
0568
0569
0570
TN
GA
NC
GA
NC
NC
NC
GA
TN
NC
AL
AL
AL
KY
CHR
CHA
CHA
CHA
NFN
NFN
NFN
CHA
CHR
NFN
NFA
NFA
NFA
LBL
0572
0573
0574
0575
0576
KY
MN
MN
MN
MN
LBL
CHI
CHI
CHI
CHI
Crayfish
7,13,35,47
7,13,35,47
7,13,35,47
7,13,35,47
5,7,13,35,47,59
5,7,13,35,47,59
5,7,13,35,47,59
1,5,7,13,26,35,47,59,125
5,7,13,35,47,59
5,7,13,35,47,59
5,7,13,35,47,59,75
5,7,13,35,47,59
7,35,47,172
1,5,7,13,26,35,47,59,122,125,168,172
1,5,7,13,26,35,47,59,122,125,172
4,7,8,28,29,62,119,122,125,168
4,7,8,11,26,27,28,29,47,62,74,119,
122,125,168
4,7,8,28,29,57,62,119
1,7,26,31,39,46,47,56,72,122,125
7,13,39,53,56
1,7,13,18,26,39,46,47,53,56,122,125,
215
1,7,13,18,26,47,53,122,125,215
7,13,39,56
7,13,39,56
7,39,46,47,53
1,7,13,39,46,47,53,56
1,7,13,39,46,47,53,56
1,7,13,39,46,47,53,56
7,39,46
1,7,13,26,46,47,53,122,125,215
1,7,13,39,46,47,53,56,125,215
72,73,118,122,162,172,175
72,118,122,162,172,175,180,227
118,122,162,172,175
26,36,37,72,73,91,118,121,162,175,
180
26,91,115,167,174,180
26,132,176
26,132,176
26,132,176
26,132,176
RWRI_BC
(total # taxa)
0.735 (4)
0.735 (4)
0.735 (4)
0.735 (4)
0.780 (6)
0.780 (6)
0.780 (6)
0.812 (9)
0.780 (6)
0.780 (6)
1.027 (7)
0.780 (6)
0.722 (4)
0.838 (12)
0.836 (11)
0.832 (10)
0.891 (15)
0.831 (8)
0.970 (11)
0.833 (5)
0.927 (13)
0.876 (10)
0.796 (4)
0.796 (4)
0.779 (5)
0.854 (8)
0.854 (8)
0.854 (8)
0.702 (3)
0.835 (10)
0.885 (10)
0.873 (7)
1.046 (8)
0.848 (5)
1.027 (11)
1.149 (6)
0.526 (3)
0.526 (3)
0.526 (3)
0.526 (3)
57
Table A5.
WS
ID
0577
0578
0579
0580
0581
0582
0583
0584
0585
0586
0587
0588
0589
0590
0591
0592
0593
0594
0595
0596
0597
0598
0599
0600
0601
0602
0603
0611
0614
continued.
State
National
Forest
MN
CHI
MN
CHI
MN
CHI
MN
CHI
MN
CHI
MN
CHI
MN
CHI
MN
CHI
MN
CHI
MN
CHI
MN
CHI
MI
OTT
MI
OTT
IL
MID
IL
MID
IL
MID
IL
MID
IL
MID
MO
MTF
MO
MTF
MO
MTF
MO
MTF
MO
MTF
MO
MTF
MO
MTF
MO
MTF
IL
SHA
MO
MTF
MS
NFM
0615
0616
0617
0618
0619
0620
0621
0622
MS
MS
AR
MO
MO
MO
MO
MO
NFM
NFM
OSF
MTF
MTF
MTF
MTF
MTF
0623
MO
MTF
0624
0625
AR
AR
OSF
OSF
Crayfish
26,132,176
26,132,176
26,132,176
26,132,168,176
26,132,168,176
26,132,168,176
26,132,168,176
26,132,168,176
26,132,168,176
26,132,168,176
26,132,168,176
163,168,176
163,168,176
26,132,163
163,176
26,163,200
163,176
26,163,200
26,43,68,142,145,164,176
26,68,142,145,164,176
26,68,142,145,164,176
26,68,142,145,164,176
26,68,142,145,164,176
26,68,127,130,142,145,164,176
26,68,127,142,145,164,176
26,142,164,176
26,82,132,162,176,180
26,42,142,164,176
26,72,91,94,117,123,158,178,180,203,
251,252
26,91,94,123,158,178,180,203,251
72,117,180,186,252
94,101,180,186
26,42,130,142,164,166,176
26,42,142,164,166,176
26,42,142,161,164,166,176
26,42,142,161,164,176
26,42,80,82,101,142,158,161,164,166,
176,180,186,251
26,80,82,101,158,164,166,180,186,
251
94,180,186
94,180,186
RWRI_BC
(total # taxa)
0.526 (3)
0.526 (3)
0.526 (3)
0.554 (4)
0.554 (4)
0.554 (4)
0.554 (4)
0.554 (4)
0.554 (4)
0.554 (4)
0.554 (4)
0.554 (3)
0.554 (3)
0.557 (3)
0.526 (2)
0.633 (3)
0.526 (2)
0.633 (3)
0.813 (7)
0.783 (6)
0.783 (6)
0.783 (6)
0.783 (6)
0.952 (8)
0.947 (7)
0.602 (4)
0.702 (6)
0.661 (5)
0.993 (12)
0.977 (9)
0.728 (5)
0.670 (4)
0.830 (7)
0.819 (6)
0.834 (7)
0.707 (6)
0.901 (14)
0.875 (10)
0.625 (3)
0.625 (3)
58
Table A5. continued.
WS State
National
Forest
ID
0626 AR
OUA
0627 AR
OSF
0628 MS
NFM
0629 MS
NFM
0630 MS
NFM
0631 MS
NFM
0632
0633
MS
MS
NFM
NFM
0634
0635
0636
0637
0638
0639
0640
0641
0642
MS
MS
MS
MS
MS
MS
MS
MS
MS
NFM
NFM
NFM
NFM
NFM
NFM
NFM
NFM
NFM
0643
0644
0645
0646
0647
0648
MS
MS
MS
MS
MS
AR
NFM
NFM
NFM
NFM
NFM
OUA
0649
AR
OUA
0650
AR
OUA
0651
AR
OUA
0652
AR
OUA
0653
AR
OUA
0654
AR
OUA
0655
AR
OUA
0656
AR
OUA
Crayfish
94,101,180,186
26,94,101,138,157,224
128,203
128,203
117,128,203,252
26,72,117,123,128,158,175,180,203,
224,252
72,128,203
26,72,91,117,123,128,180,203,224,
252
117,128,203,252
26,72,117,123,128,180,203,224,252
26,117,128,158,203,224,252
72,117,128,203,224,252
117,123,128,203
117,128,203
128,203
117,128,180,203,206,224
26,72,82,94,108,117,128,179,180,203,
206,217,224
26,72,82,128,179,203,206
82,128,138,158,180,186
128
82,128,180
82,128,180
93,109,130,132,139,143,148,151,155,
157,161,164,180,200,239,246
93,109,130,132,139,143,148,151,155,
157,161,164,180,200,239
93,109,130,132,139,143,148,151,155,
157,161,164,180,200,239
93,109,130,132,139,143,148,151,155,
157,161,164,180,200,239
93,109,130,132,139,143,148,151,155,
157,161,164,180,200,239
93,109,130,132,139,143,148,151,155,
157,161,164,180,200,239
93,109,130,132,139,143,148,151,155,
157,161,164,180,200,239
93,109,130,132,139,143,148,151,155,
157,161,164,180,200,239
93,109,130,132,139,143,148,151,155,
157,161,164,180,200,239
RWRI_BC
(total # taxa)
0.670 (4)
0.791 (6)
0.699 (2)
0.699 (2)
0.770 (4)
0.914 (11)
0.714 (3)
0.893 (10)
0.770 (4)
0.887 (9)
0.830 (7)
0.823 (6)
0.842 (4)
0.753 (3)
0.699 (2)
0.844 (6)
1.143 (13)
0.820 (7)
0.785 (6)
0.638 (1)
0.694 (3)
0.694 (3)
0.926 (16)
0.901 (15)
0.901 (15)
0.901 (15)
0.901 (15)
0.901 (15)
0.901 (15)
0.901 (15)
0.901 (15)
59
Table A5. continued.
WS State
National
Forest
ID
0657 AR
OUA
0658
AR
OUA
0659
AR
OUA
0660
AR
OUA
0661
AR
OUA
0662
AR
OUA
0663
AR
OUA
0664
AR
OUA
0665
AR
OUA
0666
AR
OUA
0667
AR
OUA
0668
0669
LA
LA
KIS
KIS
0670
LA
KIS
0671
0672
0673
0674
0675
0676
0677
0678
0679
0680
0681
0682
0683
0684
0685
0686
LA
LA
LA
LA
LA
LA
MS
MS
MS
MS
MS
MS
MS
MS
MS
MS
KIS
KIS
KIS
KIS
KIS
KIS
NFM
NFM
NFM
NFM
NFM
NFM
NFM
NFM
NFM
NFM
Crayfish
93,109,130,132,139,143,148,151,155,
157,161,164,180,200,239
93,95,109,130,132,139,143,148,151,
155,157,161,164,180,200,239
93,95,109,130,132,139,143,148,151,
155,157,161,164,180,200,239
88,91,93,95,109,132,139,157,161,164,
200,226,248
88,91,93,95,109,132,139,157,161,164,
200,226,248
88,91,93,95,132,139,157,161,164,200,
226,248
88,91,95,97,109,130,132,143,148,151,
157,164,180,226,238
91,130,132,143,151,157,164,173,180,
224,226,248
91,130,132,143,151,157,164,173,180,
224,226,248
91,130,132,143,151,157,164,173,180,
224,226,248
91,130,132,143,151,157,164,173,180,
224,226,248
26,94,101,157,180,192,222
26,80,94,101,138,157,180,186,192,
222,252
26,80,94,101,138,157,180,186,192,
222,252
26,94,101,157,180,186,192,222,252
94,102,180,248,252
94,102,180,248,252
102,180,248,252
80,94,102,157,180,186,248
80,94,102,180,186,248,252
26,49,94,158,160,252
49,94,252
158,252
158
158,252
158,252
158,252
158,252
94,158,252
94,252
RWRI_BC
(total # taxa)
0.901 (15)
0.941 (16)
0.941 (16)
0.965 (13)
0.965 (13)
0.952 (12)
1.112 (15)
0.917 (12)
0.917 (12)
0.917 (12)
0.917 (12)
0.804 (7)
0.860 (11)
0.860 (11)
0.824 (9)
0.767 (5)
0.767 (5)
0.752 (4)
0.783 (7)
0.793 (7)
0.937 (6)
0.725 (3)
0.642 (2)
0.582 (1)
0.642 (2)
0.642 (2)
0.642 (2)
0.642 (2)
0.676 (3)
0.631 (2)
60
Table A5.
WS
ID
0687
0688
0689
0690
0691
0692
0693
0694
continued.
State
National
Forest
MS
NFM
MS
NFM
MS
NFM
LA
KIS
LA
KIS
LA
KIS
LA
KIS
LA
KIS
0695
0696
LA
LA
KIS
KIS
0697
LA
KIS
0698
0699
0700
0701
0702
0703
0704
0705
0706
0707
0708
0709
0710
0711
0712
0713
0714
0715
0716
0717
0718
0719
0720
0721
0722
0723
0724
0725
0726
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
CHI
CHI
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
SUP
Crayfish
94,114,158,180
49,180
180
80,82,101,126,180,207,222,244,252
80,82,101,126,180,207,222,244,252
80,82,101,126,180,207,222,244,252
26,80,101,138,180,222,244
26,80,94,101,114,120,180,191,222,
244
26,80,101,114,180,191,222,244
26,80,94,101,114,120,180,191,222,
244
26,80,94,101,114,120,180,191,222,
244
176
176
176
26,132,168,176
26,132,168,176
26,132,168,176
26,132,168,176
26,132,168,176
26,132,168,176
26,132,168,176
26,132,168,176
26,132,168,176
26,132,168,176
26,132,168,176
26,132,168,176
26,132,168,176
168,176
168,176
168,176
168,176
168,176
132,176
132,176
132,176
176
176
176
176
176
RWRI_BC
(total # taxa)
0.814 (4)
0.680 (2)
0.455 (1)
0.890 (9)
0.890 (9)
0.890 (9)
0.779 (7)
0.885 (10)
0.859 (8)
0.885 (10)
0.885 (10)
0.432 (1)
0.432 (1)
0.432 (1)
0.554 (4)
0.554 (4)
0.554 (4)
0.554 (4)
0.554 (4)
0.554 (4)
0.554 (4)
0.554 (4)
0.554 (4)
0.554 (4)
0.554 (4)
0.554 (4)
0.554 (4)
0.496 (2)
0.496 (2)
0.496 (2)
0.496 (2)
0.496 (2)
0.502 (2)
0.502 (2)
0.502 (2)
0.432 (1)
0.432 (1)
0.432 (1)
0.432 (1)
0.432 (1)
61
Table A5. continued.
WS
State
National
Forest
ID
0727 MN
CHI
0728 MN
CHI
0729 MN
CHI
0730 MN
CHI
0731 MN
CHI
0732 MN
CHI
0733 MN
CHI
0734 MO
MTF
0735 MO
MTF
0736 MO
MTF
0737 MO
MTF
0738 MO
MTF
0739 MO
MTF
0740 MO
MTF
0741 MO
MTF
0742 MO
MTF
0743 MO
MTF
0744 AR
OSF
0745
AR
OSF
0746
AR
OSF
0747
AR
OSF
0748
AR
OSF
0749
AR
OSF
0750
AR
OSF
0751
AR
OSF
0752
AR
OSF
0753
0754
0755
0756
0757
MO
MO
MO
MO
MO
MTF
MTF
MTF
MTF
MTF
0758
MO
MTF
Crayfish
132,176
132,176
176
176
176
176
132,176
26,43,142,164,176
26,142,164,176
26,142,164,176
26,43,142,164,176
26,43,142,164,176
26,43,142,164,176
26,43,142,164,176
26,43,142,164,176
26,132,176,200
26,132,176,200
26,141,142,145,146,147,148,150,152,
153,155,176,177,214
26,141,142,145,146,147,148,150,152,
153,155,176,177,214
26,141,142,145,146,147,148,150,152,
153,155,176,177,214
26,141,142,145,146,147,148,150,152,
153,155,176,177,214
26,141,142,145,146,147,148,150,152,
153,155,176,177,214
26,141,142,145,146,147,148,150,152,
153,155,176,177,214
26,141,142,145,146,147,148,150,152,
153,155,176,177,214
26,42,141,142,145,146,147,148,150,
152,153,155,176,177,214
26,42,141,142,145,146,147,148,150,
152,153,155,176,177,214
143,153,155,176
42,141,147,153,155,176
42,141,147,153,155,176
42,141,153,155,176
42,66,141,142,145,146,147,148,152,
153,155,164,176,177
42,66,141,142,145,146,147,148,152,
153,155,164,176,177
RWRI_BC
(total # taxa)
0.502 (2)
0.502 (2)
0.432 (1)
0.432 (1)
0.432 (1)
0.432 (1)
0.502 (2)
0.696 (5)
0.602 (4)
0.602 (4)
0.696 (5)
0.696 (5)
0.696 (5)
0.696 (5)
0.696 (5)
0.633 (4)
0.633 (4)
0.866 (14)
0.866 (14)
0.866 (14)
0.866 (14)
0.866 (14)
0.866 (14)
0.866 (14)
0.876 (15)
0.876 (15)
0.703 (4)
0.745 (6)
0.745 (6)
0.728 (5)
0.911 (14)
0.911 (14)
62
Table A5. continued.
WS State
National
Forest
ID
0759 MO
MTF
0760
AR
OSF
0761
AR
OSF
0762
AR
OSF
0763
0764
0765
0766
0767
0768
0769
0770
0771
0772
0773
0774
AR
AR
AR
AR
MO
MO
MO
MO
MO
MO
MO
MO
OSF
OSF
OSF
OSF
MTF
MTF
MTF
MTF
MTF
MTF
MTF
MTF
0775
MO
MTF
0776
MO
MTF
0777
0778
0779
0780
0781
0782
MO
MO
MO
MO
MO
MO
MTF
MTF
MTF
MTF
MTF
MTF
0783
MO
MTF
0784
0785
0786
0787
0788
0789
0790
MO
MO
MO
MO
AR
AR
AR
MTF
MTF
MTF
MTF
OSF
OSF
OSF
Crayfish
42,66,141,142,145,146,147,148,152,
153,155,164,176,177
77,91,132,141,142,145,146,147,148,
150,152,153,155,157,164
77,91,132,141,142,145,146,147,148,
150,152,153,155,157,164
77,91,132,141,142,145,146,147,148,
150,152,153,155,157,164
141,142,145,146,147,152,153
141,142,145,146,147,152,153
141,142,145,146,147,152,153
141,142,145,146,147,152,153
42,141,164,176
26,42,141,164,176
26,42,141,164,176
26,42,164,176
26,42,130,164,176
26,42,130,164,176
26,42,130,164,176
26,42,80,82,101,130,158,164,176,180,
186,251
26,42,80,82,101,130,142,158,164,176,
180,186,251
26,80,82,101,130,158,164,180,186,
251
26,42,43,142,155,164,176
26,42,43,142,155,164,176
26,42,43,142,155,164,176
26,42,43,142,155,164,176
26,42,43,142,155,164,176
26,42,43,80,82,91,101,142,155,158,
164,176,180,186,251
26,42,43,80,82,91,101,142,155,158,
164,176,180,186,251
26,42,43,155,164,176
26,42,43,124,155,164,176
26,42,43,124,155,164,176
26,42,43,124,155,164,176
26,66,141,142,145,146,147,150
26,66,141,142,145,146,147,150
2,130,132,139,141,142,143,145,146,
147,151,152,153,157,161,164,180,
200,214
RWRI_BC
(total # taxa)
0.911 (14)
0.943 (15)
0.943 (15)
0.943 (15)
0.775 (7)
0.775 (7)
0.775 (7)
0.775 (7)
0.670 (4)
0.676 (5)
0.673 (5)
0.629 (4)
0.673 (5)
0.673 (5)
0.673 (5)
0.822 (12)
0.829 (13)
0.806 (10)
0.747 (7)
0.747 (7)
0.747 (7)
0.747 (7)
0.747 (7)
0.859 (15)
0.859 (15)
0.732 (6)
0.890 (7)
0.890 (7)
0.890 (7)
0.860 (8)
0.860 (8)
0.933 (19)
63
Table A5. continued.
WS State
National
Forest
ID
0791 AR
OSF
0792
AR
OSF
0793
AR
OSF
0794
AR
OSF
0795
AR
OSF
0796
AR
OSF
0797
0798
AR
AR
OUA
OUA
0799
AR
OUA
0800
0801
0802
OK
OK
OK
OUA
OUA
OUA
0803
OK
OUA
0804
AR
OSF
0805
AR
OSF
0806
AR
OSF
0807
AR
OSF
0808
AR
OSF
0809
0810
0811
0812
0813
0814
AR
AR
AR
AR
AR
AR
OSF
OSF
OSF
OSF
OSF
OSF
Crayfish
2,130,132,139,141,142,143,145,146,
147,151,152,153,157,161,164,180,
200,214
2,130,132,139,141,142,143,145,146,
147,151,152,153,157,161,164,180,
200,214
2,130,132,139,141,142,143,145,146,
147,151,152,153,157,161,164,180,
200,214
2,130,132,139,141,142,143,145,146,
147,150,151,152,153,157,161,164,
180,200,214
130,132,139,143,145,146,147,150,
151,157,161,164,180,200
130,132,139,143,145,146,147,150,
151,157,161,164,180,200
130,132,143,151,157,164,180,214,246
130,132,143,151,157,158,164,180,
244,246
130,132,143,151,157,158,164,180,
244,246
157,158,244,246
157,158,180,244,246
130,132,143,151,157,158,164,180,
244,246
130,132,143,151,157,158,164,180,
244,246
15,132,141,145,146,147,157,164,177,
214,226
15,132,141,145,146,147,157,164,177,
214,226
15,132,141,145,146,147,157,164,177,
214,226
15,132,141,145,146,147,157,164,177,
214,226
15,132,141,145,146,147,157,164,177,
214,226
132,145,146,147,157,164,214,226
132,145,146,147,157,164,214,226
132,145,146,147,157,164,214,226
132,145,146,147,157,164,214,226
132,145,146,147,157,164,214,226
132,145,146,147,157,164,214,226
RWRI_BC
(total # taxa)
0.933 (19)
0.933 (19)
0.933 (19)
0.946 (20)
0.852 (14)
0.852 (14)
0.808 (9)
0.820 (10)
0.820 (10)
0.756 (4)
0.760 (5)
0.820 (10)
0.820 (10)
0.881 (11)
0.881 (11)
0.881 (11)
0.881 (11)
0.881 (11)
0.747 (8)
0.747 (8)
0.747 (8)
0.747 (8)
0.747 (8)
0.747 (8)
64
Table A5. continued.
WS State
National
Forest
ID
0815 AR
OSF
0816 AR
OSF
0817 AR
OSF
0818 AR
OSF
0819 AR
OSF
0820 AR
OSF
0821 AR
OSF
0822 AR
OSF
0823 AR
OSF
0824 AR
OUA
0825 AR
OUA
0826 AR
OUA
0827 AR
OSF
OUA
0828 AR
0829 AR
OUA
0830 AR
OUA
0831 AR
OUA
0832 AR
OUA
0833 AR
OUA
0834 AR
OUA
0835 AR
OUA
0836 AR
OUA
0837 TX
NFT
0838 TX
NFT
0839 OK
OUA
0840 OK
OUA
0841 OK
OUA
0842
OK
OUA
0844
OK
OUA
0845
AR
OUA
0846
AR
OUA
0847
OK
OUA
0849
0850
AR
AR
OUA
OUA
0853
0854
AR
LA
OUA
KIS
Crayfish
132,145,146,147,157,164,214,226
132,145,146,147,157,164,214,226
132,145,146,147,157,164,214,226
132,145,146,147,157,164,214,226
132,145,146,147,157,164,214,226
132,145,146,147,157,164,214,226
132,145,146,147,157,164,214,226
91,132,145,146,147,157,164,214
91,132,145,146,147,157,164,214
132,157,164,180,214
132,157,164,180,214
132,157,164,214
132,157,164,214
132,157,164,214
132,157,164,214
132,157,164,214
132,157,164,180
132,157,164,180,214
132,157,164
132,157,164,214
132,157,164,180,214
130,132,143,151,157,164,180,248
157,180
157
157,158,170,244,246
120,139,157,158,170,244,246
6,91,100,101,120,157,158,180,189,
244
6,91,100,101,120,157,158,180,189,
244
6,91,100,101,120,157,158,180,189,
244
26,100,101,120,139,148,157,158,161,
200,244
26,100,101,120,139,148,157,158,161,
200,244
26,100,101,120,139,148,157,158,161,
200,244
97,139,148
26,91,100,101,120,157,158,180,189,
244
97,157,180
26,80,101,102,180,186,198,222
RWRI_BC
(total # taxa)
0.747 (8)
0.747 (8)
0.747 (8)
0.747 (8)
0.747 (8)
0.747 (8)
0.747 (8)
0.733 (8)
0.733 (8)
0.640 (5)
0.640 (5)
0.629 (4)
0.629 (4)
0.629 (4)
0.629 (4)
0.629 (4)
0.587 (4)
0.640 (5)
0.569 (3)
0.629 (4)
0.640 (5)
0.765 (8)
0.525 (2)
0.488 (1)
0.938 (5)
0.958 (7)
0.870 (10)
0.870 (10)
0.870 (10)
0.879 (11)
0.879 (11)
0.879 (11)
0.872 (3)
0.870 (10)
0.853 (3)
0.946 (8)
65
Table A5.
WS
ID
0855
0856
continued.
State
National
Forest
LA
KIS
LA
KIS
0857
LA
KIS
0858
0859
0860
LA
LA
LA
KIS
KIS
KIS
0861
0862
0863
0864
0865
0866
0867
0868
0869
0870
0871
0872
0873
0874
0875
0876
0877
0878
0879
LA
LA
LA
LA
LA
LA
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
KIS
KIS
KIS
KIS
KIS
KIS
NFT
NFT
NFT
NFT
NFT
NFT
NFT
NFT
NFT
NFT
NFT
NFT
NFT
0880
TX
NFT
0881
0882
TX
TX
NFT
NFT
0883
0884
0885
0886
0887
0888
0889
TX
TX
TX
TX
TX
TX
TX
NFT
NFT
NFT
NFT
NFT
NFT
NFT
0890
TX
NFT
Crayfish
26,80,102,180,186,198,222
26,80,82,101,120,138,144,180,186,
222,244,248
26,80,82,101,120,138,144,180,186,
222,244,248
80,82,102,180,186,207,248,252
80,94,102,180,186,248,252
26,80,82,101,120,138,144,180,186,
222,244,248
80,101,138,180,207,222,244,252
102,180
26,102,120,138,180,186,222,244,248
26,102,120,180,186,222,244,248
102,138,180,186
26,102,120,138,180,186,222,244,248
180,222
180,222
180,191,244
180,191,244
180,191,244
180,191,244
180,191
180,191,244
90,157,180,191,221,244
90,157,186,221
90,94,157,180,191,208,244
90,94,157,180,186,189,208,220
90,94,157,180,189,191,208,220,221,
244
26,90,94,157,180,186,189,191,208,
220,244
26,90,94,157,180,186,189,208,220
26,49,80,90,94,99,157,180,189,191,
208,221
26,80,90,94,157,180,189,191,208
49,80,90,94,99,157,180,189,191,208
49,99,157,180,191,221,244
26,49,99,157,180,191,208,221,244
49,99,157,180,191,208,221,244
26,49,99,157,180,191,208,244
26,49,80,94,99,157,180,191,208,221,
244
26,49,80,94,99,157,180,191,208,244
RWRI_BC
(total # taxa)
0.941 (7)
0.943 (12)
0.943 (12)
0.837 (8)
0.793 (7)
0.943 (12)
0.832 (8)
0.680 (2)
0.848 (9)
0.820 (8)
0.760 (4)
0.848 (9)
0.636 (2)
0.636 (2)
0.663 (3)
0.663 (3)
0.663 (3)
0.663 (3)
0.629 (2)
0.663 (3)
0.815 (6)
0.790 (4)
0.802 (7)
0.893 (8)
0.927 (10)
0.910 (11)
0.894 (9)
0.927 (12)
0.843 (9)
0.903 (10)
0.856 (7)
0.872 (9)
0.871 (8)
0.839 (8)
0.887 (11)
0.857 (10)
66
Table A5.
WS
ID
0891
0892
0893
0894
0895
0896
0897
0899
0900
0901
0902
0903
0904
0905
0906
0907
0908
0909
0910
0911
0912
0913
0914
0915
0916
0917
0918
0919
0920
0921
0922
0923
0924
0925
0926
0927
0928
0929
0930
0931
0932
0933
continued.
State
National
Forest
TX
NFT
TX
NFT
TX
NFT
TX
NFT
TX
NFT
TX
TX
TX
NC
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
WI
NFT
NFT
NFT
NFN
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
CHE
Crayfish
26,80,94,99,157,180,208,221
26,49,80,94,99,157,180,191,208,221
157
26,157
26,90,94,157,180,186,189,191,208,
220,244
157,186,204,208,221
157,204,208
157,204
3,7,40,41,60,180
176
176
163,176
163,176
168,176,180
168,176
163,168,176
163,176
26,163,176
168,176
176
176
163,176
26,176
163,176
168,176
163
26,176
26,163,176
176
163,176
176
163,176
26,163,176
26,163,176
26,176
168,176
163,176
176
26,163,176
163,176
26,176
163,176
RWRI_BC
(total # taxa)
0.834 (8)
0.881 (10)
0.488 (1)
0.516 (2)
0.910 (11)
0.813 (5)
0.747 (3)
0.707 (2)
0.831 (6)
0.432 (1)
0.432 (1)
0.526 (2)
0.526 (2)
0.530 (3)
0.496 (2)
0.554 (3)
0.526 (2)
0.545 (3)
0.496 (2)
0.432 (1)
0.432 (1)
0.526 (2)
0.480 (2)
0.526 (2)
0.496 (2)
0.502 (1)
0.480 (2)
0.545 (3)
0.432 (1)
0.526 (2)
0.432 (1)
0.526 (2)
0.545 (3)
0.545 (3)
0.480 (2)
0.496 (2)
0.526 (2)
0.432 (1)
0.545 (3)
0.526 (2)
0.480 (2)
0.526 (2)
67
Table A5.
WS
ID
0934
0935
0936
0937
0938
0939
0940
0941
0950
0951
0952
0953
0954
0955
0956
0957
0960
0961
0962
0963
0964
0965
0966
0967
0968
0969
0970
0971
0972
0973
0974
0975
0976
0977
0978
0979
0980
0981
0982
0983
0984
0985
continued.
State
National
Forest
WI
CHE
WI
CHE
WI
CHE
WI
CHE
WI
CHE
WI
CHE
WI
CHE
WI
CHE
TX
NFT
TX
NFT
TX
NFT
TX
NFT
TX
NFT
TX
NFT
TX
NFT
TX
NFT
FL
NFF
FL
NFF
FL
NFF
FL
NFF
FL
NFF
FL
NFF
FL
NFF
FL
NFF
FL
NFF
FL
NFF
FL
NFF
FL
NFF
FL
NFF
FL
NFF
FL
NFF
FL
NFF
FL
NFF
FL
FL
FL
FL
FL
FL
FL
FL
FL
NFF
NFF
NFF
NFF
NFF
NFF
NFF
NFF
NFF
Crayfish
26,132,163,176
176
163,168,176
26,163,168,176
163,168,176
163,176
163,176
26,163,168,176
94,157,180,186,204,208,244
49,80,94,157,180,186,204,208,244
49,80,94,157,180,186,204,208
80,157,186,204
94,157,180,186,204,208,244
80,94,157,180,186,204,244
80,94,157,180,186,204,208
26,80,94,157,180,186,204,244
196,225,233,235,242
196,225,233,235,242
196,225,233,235,242
196,225,233,235,242
196,225,233,242
196,212,225,233,242
196,225,242
196,225,233,242
72,209,212,225,233,235,245
72,209,212,225,245
72,209,212,225,235,241
196,225,233,242,245
209,212,225,245
209,212,225,235,241,245
209,212,225,240,245
209,225,240,245
26,58,209,210,225,234,235,241,245,
253
209,212,225,235,241,245
209,212,225,235,241,250
81,209,212,225,240
81,209,212,225,240,245
209,212,225,235
209,212,225
212,240
209,210,212,225,234,235,245
196,225
RWRI_BC
(total # taxa)
0.571 (4)
0.432 (1)
0.554 (3)
0.569 (4)
0.554 (3)
0.526 (2)
0.526 (2)
0.569 (4)
0.789 (7)
0.843 (9)
0.836 (8)
0.745 (4)
0.789 (7)
0.778 (7)
0.792 (7)
0.780 (8)
0.870 (5)
0.870 (5)
0.870 (5)
0.870 (5)
0.839 (4)
0.864 (5)
0.785 (3)
0.839 (4)
0.870 (7)
0.786 (5)
0.891 (6)
0.848 (5)
0.778 (4)
0.893 (6)
0.867 (5)
0.843 (4)
1.202 (10)
0.893 (6)
0.899 (6)
0.978 (5)
0.982 (6)
0.811 (4)
0.763 (3)
0.820 (2)
1.037 (7)
0.695 (2)
68
Table A5.
WS
ID
0986
0987
0988
0989
0990
0991
0992
0993
0994
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1018
1019
1023
1024
1026
1027
1027
1028
1029
1032
1033
1049
1050
1052
continued.
State
National
Forest
FL
NFF
FL
NFF
FL
NFF
FL
NFF
FL
NFF
FL
NFF
FL
NFF
FL
NFF
FL
NFF
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
SC
FMS
NY
GMF
NY
GMF
NY
GMF
VA
WJF
NY
VA
VA
VA
VA
VA
IN
IN
IN
IN
IN
OH
OH
OH
GMF
WJF
WJF
WJF
WJF
WJF
HOO
HOO
HOO
HOO
HOO
WAY
WAY
WAY
Crayfish
196,199
6,196,199
196,209,225,245
196,225
196,199
181,190,196,199
196,216,225
196,199
196,216
26,46,98,180,182,184,193
98,180,184
98,180,184,193
26,180,193,247
98,184,193,247
180,182,247
247
98
98,247
98,247
98,247
5,7,17,41,46
3,46,180
3,41,46,72,83,84,85,247
98,247
62,163
7,62,132,163
7,62,163
4,7,8,11,26,27,28,29,44,57,62,74,119,
122,125
62,163
4,7,8,28,29,62
7,16,28,62,64,119
4,7,8,28,29,62
4,7,8,28,29,62,122,125,168
4,7,8,28,29,62,119
26,163,168
168
45,168
168
168
169
169
64,119,169
RWRI_BC
(total # taxa)
0.814 (2)
1.034 (3)
0.769 (4)
0.695 (2)
0.814 (2)
1.130 (4)
0.924 (3)
0.814 (2)
0.915 (2)
1.019 (7)
0.860 (3)
0.930 (4)
0.845 (4)
0.943 (4)
0.920 (3)
0.666 (1)
0.719 (1)
0.772 (2)
0.772 (2)
0.772 (2)
0.786 (5)
0.602 (3)
1.094 (8)
0.772 (2)
0.557 (2)
0.596 (4)
0.577 (3)
1.063 (15)
0.557 (2)
0.792 (6)
0.734 (6)
0.792 (6)
0.826 (9)
0.800 (7)
0.554 (3)
0.460 (1)
0.704 (2)
0.460 (1)
0.460 (1)
0.607 (1)
0.607 (1)
0.696 (3)
69
Table A5.
WS
ID
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1064
1065
1067
1069
1070
1071
1072
1073
1074
1075
1076
1077
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
continued.
State
National
Forest
OH
WAY
OH
WAY
OH
WAY
OH
WAY
OH
WAY
OH
WAY
OH
WAY
OH
WAY
OH
WAY
OH
WAY
OH
WAY
OH
WAY
OH
WAY
OH
WAY
OH
WAY
OH
WAY
OH
WAY
OH
WAY
WAY
OH
OH
WAY
IL
SHA
IL
SHA
IL
SHA
IL
SHA
IL
SHA
IL
SHA
IL
SHA
IL
SHA
IL
SHA
IL
SHA
IL
SHA
IL
SHA
IL
SHA
IL
IL
SHA
SHA
Crayfish
64,74,119,169
74,169
71,74,119,169
74,169
74,119,169
71,74,169
74,119
71,74,119,169
62,71
74,168,169
62,71,74,169
74,168,169
74,168,169
74,169
71,74,169
71,74,169
71,74,119,169
168,169
62,74,168,169
74,168,169
26,82,91,131,132,162,176,180,186
26,73,132
26,132,138,162,176,180
26,91,131,176
26,162,176,180,186,251
26,91,131,132,180
26,131,132,133,180
26,73,176
132
26,82,132,162,176,180
82,91,131,132,186
73,131,163,251
26,80,82,91,131,132,138,176,180,186,
251
132,162,180
26,82,132,180,251
RWRI_BC
(total # taxa)
0.723 (4)
0.658 (2)
0.785 (4)
0.658 (2)
0.678 (3)
0.777 (3)
0.615 (2)
0.785 (4)
0.741 (2)
0.667 (3)
0.783 (4)
0.667 (3)
0.667 (3)
0.658 (2)
0.777 (3)
0.777 (3)
0.785 (4)
0.620 (2)
0.681 (4)
0.667 (3)
0.795 (9)
0.665 (3)
0.736 (6)
0.720 (4)
0.745 (6)
0.728 (5)
0.820 (5)
0.661 (3)
0.468 (1)
0.702 (6)
0.760 (5)
0.794 (4)
0.847 (11)
0.647 (3)
0.722 (5)
70
Table A6. A list of the National Forests in the eastern United States.
National Forest Abbreviation
National Forest
ALL
Allegheny
CHA
Chattahoochee-Oconee
CHE
Chequamegon-Nicolet
CHI
Chippewa
CHR
Cherokee
DBF
Daniel Boone
FMS
Francis Marion-Sumter
GMF
Green Mountain
HIA
Hiawatha
HMF
Huron-Manistee
HOO
Hoosier
KIS
Kisatchie
LBL
Land Between the Lakes
MON
Monongahela
MID
Midewin
MTF
Mark Twain
NFA
National Forest of Alabama
NFF
National Forest of Florida
NFM
National Forest of Mississippi
National Forest of North Carolina
NFN
NFT
National Forest of Texas
OTT
Ottawa
OUA
Ouachita
OSF
Ozark-St. Francis
SHA
Shawnee
SUP
Superior
WAY
Wayne
WJF
George Washington-Jefferson
WMF
White Mountain
71
Table A7. Watershed categories for the growth rate of human populations. Watershed
categories are based on population growth rates (High = upper 25th percentile, Medium =
25th to 75th percentile, Low = 25th percentile) for 3 time periods (current = 1990 – 2000,
recent = 1950 – 1990, historic = 1790 – 1940).
1990-2000
1950-1990
1790-1940
Code
Category
High
High
High
HHH
25
High
High
Medium
HHM
24
High
High
Low
HHL
23
High
Medium
High
HMH
22
High
Medium
Medium
HMM
21
High
Medium
Low
HML
20
High
Low
High
HLH
19
High
Low
Medium
HLM
18
Medium
High
High
MHH
17
Medium
High
Medium
MHM
16
Medium
High
Low
MHL
15
Medium
Medium
High
MMH
14
Medium
Medium
Medium
MMM
13
Medium
Medium
Low
MML
12
Medium
Low
High
MLH
11
Medium
Low
Medium
MLM
10
Medium
Low
Low
MLL
9
Low
High
High
LHH
8
Low
High
Medium
LHM
7
Low
Medium
High
LMH
6
Low
Medium
Medium
LMM
5
Low
Medium
Low
LML
4
Low
Low
High
LLH
3
Low
Low
Medium
LLM
2
Low
Low
Low
LLL
1
72
Table A8. Candidate metrics (50) screened for final risk assessment. Metric screening was
conducted for completeness (was data available for all the watersheds), redundancy (was the
correlation between metrics greater than 0.75), range (metrics measured in percentages greater
than 10%), and relationship (positive correlation with dependent variable). An X means that
metric did not pass that part of the metric screening. An P in the final column means that the
metric passed the screening process and was used in the final risk assessment. (Description of
metrics given in Table A10)
Metric
dam_a
res_sa_a
res_v_a
res_age
damxh_a
pop_cat
den_2000
road_den
xing_a
xing_stm
x0_1_stm
x1_2_stm
b66_rddn
b66_pstm
b30_rddn
b30_pstm
b66_pg01
b30_pg01
b66_pg12
b30_pg12
no_3_dep
so_4_dep
water
res_low
res_high
com_ind
bare
mine
trans
desfor
evefor
mixfor
shrub
nnat_wod
grass
past
crop
grain
urec
Completeness
Redundancy
Range
Relationship
Final
P
X
X
X
X
P
P
P
P
X
X
P
X
X
P
X
X
X
X
X
P
X
X
X
P
X
X
P
X
X
X
X
X
X
X
X
X
X
X
73
Table A8. continued.
Metric
Completeness
wwet
hwet
watr_wet
urban
bar_min
forest
nshrub
ngrass
agricult
natural
human
Redundancy
X
Range
Relationship
Final
X
X
X
X
X
X
X
X
X
P
74
Table A9. Scores for the individual metrics (weighting factors), final watershed score (sum of each metric multiplied times its
weighting factor), and final watershed risk category used in the crayfish risk assessment. The WS ID is the individual watershed code
that we developed for the watersheds. (Description of metrics given in Table A10)
res_ Mine Human Watershed Category
no_
b30_
x1_
WS
dam_ pop_ den_ road_ xing_
Scores
2_stm rddn 3_dep high
a
den
2000
cat
ID
a
(2)
(7)
(2)
(17)
(0.1)
(1)
(17)
(17)
(8)
(29)
(12)
0001
4
1
3
1
1
1
3
2
4
3
1
201.3
low
0002
3
1
2
1
1
2
3
2
3
3
1
180.3
very low
0003
3
2
2
1
1
2
3
1
2
3
1
190.3
very low
0004
4
4
4
4
4
4
4
2
4
4
3
412.4
very high
0005
1
2
2
1
1
3
4
3
2
3
1
201.4
low
0006
4
2
2
1
2
3
3
3
1
3
1
252.3
low
0007
4
4
4
3
3
4
3
2
3
4
2
374.3
very high
0008
2
2
2
1
2
3
2
2
2
4
2
222.2
low
0009
1
2
2
2
3
3
2
4
2
3
1
269.2
medium
0010
3
2
1
1
1
1
1
4
2
4
1
239.1
low
0011
2
2
2
2
3
4
3
4
3
4
1
291.3
medium
0012
4
3
3
3
3
4
3
4
4
3
1
364.3
very high
0013
4
2
3
2
3
4
3
4
2
3
1
314.3
high
0014
3
1
2
1
1
2
2
3
3
3
1
197.2
very low
0015
1
1
2
1
2
2
3
3
3
4
2
199.3
very low
0016
4
1
3
2
1
2
3
3
3
4
2
243.3
low
0017
3
2
2
3
2
3
3
4
1
3
1
291.3
medium
0018
4
3
3
2
2
3
4
4
4
4
1
336.4
high
0019
4
2
3
3
3
4
4
4
3
3
2
335.4
high
0020
4
3
3
2
2
4
4
4
3
3
3
332.4
high
0022
3
2
3
3
3
4
4
4
1
3
2
319.4
high
0023
4
3
4
3
4
4
3
4
4
3
3
393.3
very high
0025
1
3
3
3
4
4
4
4
1
3
3
343.4
high
0026
4
2
3
3
2
3
4
4
3
3
2
317.4
high
0028
3
3
4
2
2
3
4
4
4
3
3
329.4
high
den_
2000
(8)
4
4
2
2
2
2
3
4
4
3
2
2
1
2
3
2
1
2
4
3
4
4
4
2
4
4
3
road_
den
(17)
3
3
2
2
2
1
2
3
3
3
1
2
1
1
2
2
1
2
4
3
4
4
4
1
4
4
4
xing_
a
(17)
3
2
3
3
3
4
3
3
3
3
1
3
2
2
3
3
2
3
2
2
4
3
2
2
2
3
4
x1_
2_stm
(1)
4
4
3
4
4
4
3
4
4
4
2
3
1
1
2
1
2
3
4
3
4
4
4
4
4
4
4
b30_
rddn
(0.1)
4
4
4
4
4
4
4
4
4
4
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
no_
3_dep
(17)
4
3
4
3
4
4
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
3
3
3
3
3
4
res_
high
(2)
4
4
3
2
2
2
2
4
4
3
1
2
1
3
3
1
2
1
3
1
4
3
3
1
3
3
2
Mine
Human
(7)
3
4
3
3
3
3
3
4
3
3
3
1
4
1
4
1
1
1
4
1
4
4
1
1
4
2
4
(2)
2
4
2
2
2
2
3
2
2
2
1
3
2
3
4
3
2
2
4
3
4
4
4
2
4
4
3
Watershed
Scores
Category
362.4
368.4
297.4
291.4
296.4
284.4
293.4
323.4
316.4
335.4
251.3
230.4
203.4
254.4
344.4
308.4
250.4
267.4
388.4
318.4
448.4
400.4
362.4
229.4
359.4
386.4
422.4
very high
very high
medium
medium
medium
medium
medium
high
high
high
low
low
low
low
high
high
low
medium
very high
high
very high
very high
very high
low
high
very high
very high
75
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0030
3
3
0031
3
4
0032
3
2
0033
4
2
0034
3
2
0035
2
2
0036
1
3
0038
4
1
0040
4
1
0041
4
2
0042
4
2
0043
1
1
0044
1
1
0045
1
3
0046
3
3
0047
3
3
0048
4
2
0049
2
2
0050
2
4
0051
3
3
0053
4
4
0054
3
4
0055
3
4
0056
3
2
0057
1
4
0058
3
4
0059
3
4
den_
2000
(8)
2
2
1
4
1
1
3
2
2
1
2
4
3
2
4
2
3
3
4
4
3
4
2
4
4
3
4
road_
den
(17)
2
2
1
3
1
1
3
1
4
2
3
4
4
4
4
4
4
4
4
4
4
4
2
4
4
3
4
xing_
a
(17)
3
4
1
4
2
1
4
3
4
2
2
4
4
4
4
4
4
4
4
4
4
4
1
2
3
2
2
x1_
2_stm
(1)
3
3
2
3
2
2
4
3
4
4
2
4
4
4
3
3
2
3
4
4
2
3
1
1
1
1
1
b30_
rddn
(0.1)
4
4
4
4
3
4
4
3
4
4
4
4
4
4
4
4
4
4
4
4
3
3
2
3
4
3
3
no_
3_dep
(17)
2
2
4
3
4
4
2
2
3
4
4
3
4
3
3
4
3
4
1
2
3
3
1
1
1
1
1
res_
high
(2)
1
1
1
3
1
1
2
1
2
1
2
3
2
1
3
1
3
1
2
4
1
2
2
4
4
4
4
Mine
Human
(7)
1
1
1
1
1
1
4
1
4
1
1
4
3
1
1
4
1
1
4
4
4
4
4
3
1
1
4
(2)
2
2
2
2
2
3
3
2
3
2
2
4
4
3
3
3
3
2
3
4
4
4
2
3
3
3
3
Watershed
Scores
Category
204.4
221.4
190.4
275.4
195.3
168.4
347.4
245.3
327.4
243.4
297.4
376.4
330.4
287.4
405.4
324.4
338.4
367.4
355.4
402.4
391.3
426.3
191.2
286.3
330.4
247.3
322.3
low
low
very low
medium
very low
very low
high
low
high
low
medium
very high
high
medium
very high
high
high
very high
high
very high
very high
very high
very low
medium
high
low
high
76
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0060
2
1
0061
2
1
0062
3
1
0063
2
1
0064
2
1
0065
1
1
0066
1
4
0067
2
3
0068
2
2
0069
2
2
0070
2
3
0071
2
3
0072
3
1
0073
3
1
0074
4
4
0075
3
1
0076
4
2
0077
3
3
0078
1
4
0079
3
4
0080
2
4
0081
4
4
0082
1
2
0083
1
3
0084
2
4
0085
1
3
0086
1
4
den_
2000
(8)
4
4
3
3
3
4
4
2
4
3
4
4
3
2
4
4
3
3
3
4
3
3
4
3
3
4
2
road_
den
(17)
3
4
3
3
4
4
4
3
4
2
4
4
2
3
4
4
3
3
4
4
3
3
4
3
2
4
4
xing_
a
(17)
2
4
4
2
2
3
2
3
4
3
3
4
3
1
1
1
1
1
1
2
1
1
2
1
1
2
3
x1_
2_stm
(1)
1
3
4
3
2
3
2
2
4
2
4
3
3
1
1
2
2
1
1
4
1
1
3
2
1
4
4
b30_
rddn
(0.1)
2
3
4
3
2
3
2
2
4
3
3
3
4
2
2
2
1
1
2
3
1
2
2
1
1
2
3
no_
3_dep
(17)
1
3
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
res_
high
(2)
4
4
2
2
3
4
4
3
4
1
3
4
2
3
4
4
3
4
3
4
4
2
4
2
2
4
1
Mine
Human
(7)
1
3
1
4
4
3
2
1
4
4
3
4
2
3
4
3
4
3
4
3
1
3
4
1
1
4
4
(2)
3
4
2
3
3
3
3
3
2
1
2
3
1
3
3
3
3
3
3
4
3
3
3
3
2
4
1
Watershed
Scores
Category
296.2
423.3
353.4
324.3
359.2
404.3
379.2
289.2
427.4
346.3
401.3
428.3
294.4
304.2
288.2
282.2
308.1
314.1
336.2
390.3
230.1
298.2
394.2
297.1
265.1
397.2
357.3
medium
very high
high
high
high
very high
very high
medium
very high
high
very high
very high
medium
medium
medium
medium
high
high
high
very high
low
medium
very high
medium
medium
very high
high
77
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0087
2
4
0088
4
4
0089
3
3
0090
3
3
0091
2
4
0092
4
4
0093
4
4
0094
1
3
0095
4
4
0096
3
4
0097
4
4
0098
4
4
0099
2
3
0101
4
3
0102
4
1
0103
4
1
0104
3
3
0105
4
3
0106
4
3
0107
4
4
0108
3
1
0109
3
3
0110
4
4
0111
4
3
0112
3
3
0113
4
4
0114
4
3
den_
2000
(8)
1
4
4
3
2
3
2
3
4
4
4
4
2
3
4
2
4
3
3
3
4
3
3
4
3
3
4
road_
den
(17)
1
4
4
3
2
4
3
3
4
4
2
4
4
3
3
2
4
4
4
3
3
3
2
3
2
3
4
xing_
a
(17)
2
4
4
3
3
3
1
3
3
3
1
3
3
2
2
1
3
2
2
2
2
2
1
2
1
4
4
x1_
2_stm
(1)
2
3
4
3
3
2
2
2
2
2
2
2
3
1
1
1
2
2
3
2
1
2
1
2
1
4
4
b30_
rddn
(0.1)
3
3
4
3
3
3
1
1
2
3
1
2
2
1
1
1
2
2
2
2
2
1
1
2
1
4
3
no_
3_dep
(17)
3
3
3
3
3
3
2
3
3
3
1
2
2
1
2
1
2
1
1
1
1
1
1
1
1
3
3
res_
high
(2)
1
4
4
3
2
1
3
4
4
3
2
4
3
3
4
2
3
4
3
2
4
3
3
4
3
2
3
Mine
Human
(7)
4
1
4
3
1
1
4
3
1
4
3
3
1
3
2
4
3
4
3
4
4
3
4
2
4
3
4
(2)
1
2
4
1
1
1
2
4
3
4
1
3
3
3
4
3
4
3
3
4
2
3
2
3
2
1
3
Watershed
Scores
Category
250.3
405.3
431.4
373.3
332.3
342.3
223.1
351.1
389.2
410.3
293.1
386.2
285.2
295.1
346.1
217.1
386.2
322.2
343.2
332.2
327.2
325.1
237.1
328.2
295.1
389.4
427.3
low
very high
very high
very high
high
high
low
high
very high
very high
medium
very high
medium
medium
high
low
very high
high
high
high
high
high
low
high
medium
very high
very high
78
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0115
4
2
0116
4
4
0117
4
4
0118
4
4
0119
4
4
0121
4
3
0122
3
1
0123
4
3
0124
4
4
0125
4
4
0126
4
4
0127
4
4
0128
3
2
0129
4
3
0130
4
4
0131
3
2
0132
4
4
0133
4
3
0134
4
4
0135
4
4
0136
3
4
0137
4
4
0138
4
2
0139
4
4
0140
4
4
0141
4
4
0142
4
4
den_
2000
(8)
4
4
3
1
4
2
1
1
3
3
3
4
4
4
4
3
4
3
4
4
2
3
3
4
3
4
4
road_
den
(17)
4
4
3
2
3
2
2
3
2
2
2
4
4
3
4
3
4
3
3
4
1
2
2
3
2
4
3
xing_
a
(17)
4
4
3
3
3
2
2
2
3
3
3
4
4
3
4
4
4
3
3
4
3
4
3
3
3
4
4
x1_
2_stm
(1)
4
4
2
1
2
2
1
2
3
2
3
3
4
3
4
3
4
3
3
3
3
3
3
2
3
4
4
b30_
rddn
(0.1)
3
4
2
1
1
1
1
1
1
2
2
3
4
3
3
3
4
3
3
3
1
2
2
2
2
3
3
no_
3_dep
(17)
3
3
1
1
1
1
1
1
1
1
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
res_
high
(2)
3
3
4
1
4
1
1
1
3
3
2
4
4
4
4
2
2
1
3
4
1
1
1
2
1
4
4
Mine
Human
(7)
3
4
1
1
1
1
1
3
1
3
4
3
4
3
1
3
4
1
4
3
1
4
4
1
1
4
3
(2)
2
2
4
3
4
3
2
2
3
2
3
4
4
2
3
1
1
1
1
4
2
3
3
2
1
3
3
Watershed
Scores
Category
418.3
425.4
245.2
167.1
229.1
200.1
189.1
245.1
213.1
270.2
341.2
423.3
431.4
385.3
396.3
388.3
421.4
355.3
388.3
423.3
286.1
380.2
322.2
366.2
309.2
429.3
405.3
very high
very high
low
very low
low
very low
very low
low
low
medium
high
very high
very high
very high
very high
very high
very high
high
very high
very high
medium
very high
high
very high
high
very high
very high
79
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0143
4
4
0144
4
4
0145
4
1
0146
1
1
0147
2
1
0148
2
2
0149
2
2
0150
4
2
0151
3
1
0152
2
3
0153
2
4
0154
4
4
0155
4
4
0156
4
4
0157
3
4
0158
4
4
0159
4
4
0160
4
4
0161
4
4
0162
4
4
0163
4
3
0164
4
4
0165
3
3
0166
4
4
0167
4
3
0168
4
4
0169
4
4
den_
2000
(8)
3
2
3
4
4
4
4
2
2
3
2
2
4
4
2
3
3
1
1
3
2
3
2
1
3
1
3
road_
den
(17)
3
2
1
4
3
4
4
2
2
4
3
1
4
4
2
3
1
1
1
3
3
3
1
1
2
1
1
xing_
a
(17)
3
4
2
4
3
3
4
3
2
4
3
2
4
3
2
3
2
2
2
4
3
4
2
1
2
1
1
x1_
2_stm
(1)
3
4
2
4
4
3
3
2
2
4
3
2
3
2
2
2
2
2
2
1
1
2
1
1
1
1
1
b30_
rddn
(0.1)
2
3
1
4
3
3
3
2
1
4
3
1
3
2
1
2
1
1
1
2
2
2
1
1
1
1
1
no_
3_dep
(17)
3
3
3
3
3
2
2
2
3
3
3
3
1
1
1
1
1
1
1
3
3
3
1
3
3
3
3
res_
high
(2)
4
1
1
4
3
4
4
1
2
4
2
1
4
4
2
3
3
1
1
3
1
4
2
1
1
1
1
Mine
Human
(7)
3
4
1
4
1
3
4
1
3
3
1
1
4
4
3
3
1
1
1
1
3
1
3
1
1
1
1
(2)
3
2
2
3
3
2
3
2
1
2
2
2
3
3
2
2
2
2
2
4
4
4
2
1
2
1
2
Watershed
Scores
Category
350.2
342.3
276.1
342.4
343.3
385.3
411.3
285.2
299.1
412.4
322.3
256.1
394.3
364.2
243.1
316.2
251.1
144.1
144.1
368.2
336.2
342.2
191.1
187.1
280.1
199.1
222.1
high
high
medium
high
high
very high
very high
medium
medium
very high
high
low
very high
very high
low
high
low
very low
very low
very high
high
high
very low
very low
medium
very low
low
80
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0170
4
3
0171
4
3
0172
4
3
0173
4
1
0174
4
3
0175
4
4
0176
4
4
0177
4
3
0178
4
3
0179
4
4
0180
4
3
0181
3
3
0182
4
4
0183
3
4
0184
2
3
0185
2
4
0186
2
4
0187
2
1
0188
2
1
0189
3
4
0190
4
3
0191
3
3
0192
4
1
0193
2
2
0194
3
3
0195
3
2
0196
1
3
den_
2000
(8)
3
3
3
2
2
1
2
2
1
2
2
3
4
2
4
2
2
2
2
1
4
4
2
1
3
1
4
road_
den
(17)
2
2
3
2
2
1
2
2
2
2
1
2
4
3
3
1
1
1
1
1
3
3
3
2
3
2
3
xing_
a
(17)
2
1
2
2
3
2
3
3
2
3
3
3
4
3
3
3
3
2
2
2
3
3
3
1
3
3
3
x1_
2_stm
(1)
1
1
2
2
2
2
2
1
1
1
1
2
4
1
2
2
2
2
1
1
3
2
1
1
2
2
2
b30_
rddn
(0.1)
1
1
1
2
3
1
1
1
1
1
1
2
4
2
1
1
1
1
1
1
2
2
1
1
2
1
1
no_
3_dep
(17)
3
3
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
res_
high
(2)
3
2
1
1
3
1
3
1
1
1
2
2
4
4
3
1
3
2
1
1
3
3
2
1
3
1
3
Mine
Human
(7)
1
4
1
1
1
1
1
1
1
3
1
3
4
4
3
3
4
1
1
1
3
4
1
1
4
1
3
(2)
3
2
3
2
3
2
2
2
3
3
3
3
4
2
3
3
3
2
2
1
2
4
3
2
3
3
3
Watershed
Scores
Category
286.1
262.1
288.1
227.2
204.3
144.1
214.1
255.1
203.1
283.1
172.1
282.2
397.4
299.2
321.1
267.1
278.1
224.1
221.1
158.1
337.2
359.2
247.1
172.1
349.2
233.1
350.1
medium
medium
medium
low
low
very low
low
low
low
medium
very low
medium
very high
medium
high
medium
medium
low
low
very low
high
high
low
very low
high
low
high
81
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0197
3
3
0198
1
3
0199
2
3
0200
2
3
0201
3
1
0202
2
1
0203
4
1
0204
3
3
0205
3
2
0206
4
3
0207
2
1
0208
3
3
0209
4
4
0210
3
3
0211
4
3
0212
4
3
0213
4
3
0214
3
3
0215
3
3
0216
1
2
0217
3
4
0218
4
4
0219
3
2
0220
2
2
0221
4
4
0222
4
2
0224
4
4
den_
2000
(8)
4
3
3
4
3
2
2
2
4
3
2
1
1
1
1
1
1
2
1
1
1
1
2
2
2
2
1
road_
den
(17)
3
3
2
3
2
2
3
1
3
2
2
1
1
1
1
2
1
2
1
1
1
1
1
1
2
2
1
xing_
a
(17)
2
3
4
3
3
3
4
3
4
3
3
2
1
1
1
2
1
2
1
1
1
1
1
1
2
1
1
x1_
2_stm
(1)
1
4
2
2
3
2
2
2
2
1
2
2
1
1
1
1
1
2
1
1
1
1
1
1
3
4
1
b30_
rddn
(0.1)
1
2
2
2
1
2
2
1
2
1
1
2
1
1
1
3
2
3
1
1
1
1
1
1
3
2
2
no_
3_dep
(17)
1
1
1
1
1
1
1
1
1
1
1
3
2
2
2
2
1
1
1
1
1
2
3
1
1
1
1
res_
high
(2)
3
4
2
4
1
2
4
1
4
3
2
1
1
1
1
1
1
4
1
1
1
2
2
2
4
4
1
Mine
Human
(7)
1
1
3
1
1
1
3
1
3
1
3
3
1
4
1
3
1
3
3
1
3
3
4
4
4
4
1
(2)
2
4
4
4
4
3
3
3
4
3
4
1
1
1
1
1
1
1
1
1
1
1
1
3
3
2
1
Watershed
Scores
Category
280.1
247.2
313.2
340.2
281.1
260.2
266.2
229.1
371.2
269.1
288.1
207.2
158.1
191.1
170.1
206.3
141.2
262.3
155.1
141.1
155.1
174.1
271.1
171.1
211.3
181.2
136.2
medium
low
high
high
medium
medium
medium
low
very high
medium
medium
low
very low
very low
very low
low
very low
medium
very low
very low
very low
very low
medium
very low
low
very low
very low
82
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0225
1
4
0226
4
1
0227
4
3
0228
4
4
0229
4
3
0230
3
3
0231
4
1
0232
2
3
0233
4
4
0234
3
3
0235
4
3
0236
1
2
0237
1
2
0238
2
2
0239
2
2
0240
1
2
0241
1
2
0242
1
4
0243
1
2
0245
1
2
0246
1
2
0247
1
2
0248
4
3
0249
3
1
0250
3
1
0251
2
1
0252
3
1
den_
2000
(8)
1
4
4
1
1
3
1
1
1
1
1
1
1
1
1
2
4
2
3
1
1
1
2
2
1
1
2
road_
den
(17)
3
4
4
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
1
2
2
1
2
1
1
2
xing_
a
(17)
2
3
3
1
3
1
1
2
2
1
1
1
2
1
2
1
2
2
1
1
1
1
1
2
1
1
1
x1_
2_stm
(1)
1
2
4
1
1
2
1
1
2
2
2
1
2
2
2
1
2
3
2
1
1
1
2
2
1
3
4
b30_
rddn
(0.1)
3
3
3
1
1
2
1
1
1
1
1
1
1
1
1
1
2
2
3
1
1
2
1
2
2
1
2
no_
3_dep
(17)
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
4
4
1
1
1
1
2
2
res_
high
(2)
2
4
4
1
1
4
2
2
2
2
2
4
3
2
2
2
4
4
4
2
2
1
3
4
3
2
4
Mine
Human
(7)
3
4
4
1
1
4
1
4
1
2
1
3
3
1
3
3
4
2
4
2
2
1
2
4
4
2
3
(2)
3
4
3
1
1
1
1
1
1
2
1
1
1
1
2
1
2
2
1
1
1
1
1
2
1
1
2
Watershed
Scores
Category
183.3
279.3
279.3
170.1
146.1
168.2
114.1
164.1
144.1
136.1
127.1
144.1
160.1
156.1
218.1
160.1
224.2
200.2
190.3
201.1
230.1
129.2
144.1
196.2
149.2
169.1
249.2
very low
medium
medium
very low
very low
very low
very low
very low
very low
very low
very low
very low
very low
very low
low
very low
low
very low
very low
low
low
very low
very low
very low
very low
very low
low
83
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0253
1
1
0254
3
1
0255
3
1
0256
2
2
0257
1
1
0259
2
1
0260
1
1
0261
2
1
0262
2
1
0263
2
1
0264
2
1
0265
2
1
0266
2
1
0267
2
2
0268
2
3
0269
3
1
0270
3
1
0271
2
1
0272
1
1
0273
1
2
0274
2
2
0275
1
1
0276
2
1
0277
2
1
0278
2
1
0279
1
2
0280
2
3
den_
2000
(8)
1
1
4
3
4
3
2
4
2
3
4
4
3
4
4
2
2
3
3
2
1
1
2
2
1
1
2
road_
den
(17)
2
2
4
3
4
3
3
4
2
3
4
3
4
4
4
3
3
4
3
3
1
3
3
1
1
2
2
xing_
a
(17)
1
1
3
2
2
3
2
2
2
2
2
3
3
3
3
2
2
2
1
1
1
1
1
2
1
1
1
x1_
2_stm
(1)
1
4
4
4
4
4
3
4
3
3
4
4
4
3
3
4
3
4
3
4
1
1
4
1
1
4
4
b30_
rddn
(0.1)
2
2
3
3
3
3
2
2
2
3
3
3
3
3
3
3
3
3
2
3
1
2
3
2
1
2
3
no_
3_dep
(17)
3
3
3
3
4
3
3
3
3
3
3
3
3
3
4
3
3
3
3
3
3
3
4
4
4
4
4
res_
high
(2)
2
1
4
3
3
3
2
4
2
3
4
4
4
4
4
3
3
3
4
2
2
2
4
3
2
3
3
Mine
Human
(7)
1
3
1
3
4
3
3
2
1
3
2
1
1
1
3
2
4
1
4
3
4
2
3
2
1
2
1
(2)
2
1
4
4
3
4
3
3
3
2
4
4
4
4
4
2
3
3
2
2
1
1
1
4
1
2
1
Watershed
Scores
Category
196.2
209.2
369.3
339.3
398.3
368.3
297.2
357.2
237.2
305.3
359.3
364.3
373.3
368.3
399.3
291.3
306.3
311.3
326.2
279.3
181.1
201.2
298.3
263.2
194.1
225.2
224.3
very low
low
very high
high
very high
very high
medium
high
low
medium
high
very high
very high
very high
very high
medium
medium
high
high
medium
very low
low
medium
medium
very low
low
low
84
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0283
1
2
0284
1
2
0285
2
4
0286
2
4
0287
3
4
0288
3
4
0289
2
3
0290
2
4
0291
2
2
0292
2
3
0293
2
4
0294
3
4
0295
3
4
0296
2
4
0297
2
4
0298
2
3
0299
2
3
0300
2
3
0301
2
4
0302
2
3
0303
2
1
0304
2
1
0305
2
3
0306
1
3
0307
1
2
0308
1
2
0309
1
2
den_
2000
(8)
4
1
2
3
2
3
2
3
2
3
3
3
4
2
1
4
3
2
2
3
2
1
2
3
1
3
1
road_
den
(17)
4
2
2
4
4
3
4
3
3
4
3
3
2
1
1
3
3
1
1
1
1
1
1
2
1
1
1
xing_
a
(17)
2
1
2
1
1
1
2
2
2
3
3
3
4
1
2
3
3
2
2
1
1
1
1
2
1
1
2
x1_
2_stm
(1)
4
3
3
4
3
2
3
1
3
3
4
3
4
1
3
4
3
3
2
1
2
2
1
2
1
1
2
b30_
rddn
(0.1)
3
2
2
3
2
3
3
2
3
3
3
3
4
1
3
4
3
2
3
2
2
3
1
3
1
2
2
no_
3_dep
(17)
3
4
3
4
3
4
3
3
3
3
2
2
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
res_
high
(2)
4
2
2
4
4
4
3
4
4
4
3
4
3
2
1
3
3
2
2
2
3
3
1
2
2
1
3
Mine
Human
(7)
4
1
2
1
3
4
2
2
2
1
4
4
3
3
1
4
3
4
1
4
1
1
1
4
4
1
1
(2)
3
1
2
1
1
2
2
3
1
2
4
3
2
1
1
2
4
1
1
1
1
1
1
1
2
1
1
Watershed
Scores
Category
284.3
225.2
271.2
338.3
297.2
342.3
278.3
300.2
244.3
281.3
271.3
357.3
290.4
187.1
194.3
297.4
273.3
213.2
249.3
202.2
188.2
168.3
241.1
237.3
217.1
249.2
255.2
medium
low
medium
high
medium
high
medium
medium
low
medium
medium
high
medium
very low
very low
medium
medium
low
low
low
very low
very low
low
low
low
low
low
85
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0311
2
1
0313
2
2
0314
2
3
0315
2
4
0316
2
3
0317
2
4
0318
2
2
0319
2
3
0321
3
1
0322
3
1
0323
3
1
0324
3
4
0325
2
1
0326
1
1
0327
2
1
0328
2
1
0329
1
1
0330
1
1
0331
1
3
0332
1
1
0333
2
1
0334
1
1
0335
2
3
0336
1
1
0337
1
2
0338
2
3
0339
2
3
den_
2000
(8)
2
4
4
2
1
1
1
1
2
2
2
2
3
4
4
4
4
3
4
3
3
4
2
4
3
4
4
road_
den
(17)
2
4
4
1
1
1
1
1
1
1
3
4
4
4
4
4
4
4
4
4
4
4
4
4
3
4
4
xing_
a
(17)
3
3
4
1
1
2
1
1
2
2
2
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
x1_
2_stm
(1)
2
4
4
1
1
3
1
1
2
2
2
2
4
4
4
3
4
3
4
4
4
4
3
4
4
4
4
b30_
rddn
(0.1)
4
4
4
3
2
4
2
3
2
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
no_
3_dep
(17)
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
3
3
3
3
2
3
2
1
res_
high
(2)
1
2
2
1
1
1
1
1
1
1
1
2
2
4
4
4
4
2
4
3
3
3
2
3
2
3
3
Mine
Human
(7)
3
4
4
1
1
4
3
1
4
1
4
4
3
4
4
3
4
3
3
3
2
3
3
4
2
3
3
(2)
1
2
3
3
2
3
2
1
2
1
2
2
3
4
3
3
4
3
4
2
4
4
3
4
4
4
4
Watershed
Scores
Category
307.4
382.4
377.4
187.3
165.2
207.4
208.2
192.3
248.2
201.4
246.4
282.4
328.4
448.4
434.4
409.4
448.4
373.4
388.4
357.4
378.4
398.4
278.4
371.4
347.4
340.4
364.4
high
very high
very high
very low
very low
low
low
very low
low
low
low
medium
high
very high
very high
very high
very high
very high
very high
high
very high
very high
medium
very high
high
high
very high
86
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0340
2
3
0341
3
3
0342
1
3
0343
2
1
0344
1
1
0345
1
1
0346
1
2
0347
1
2
0348
4
1
0349
2
1
0350
1
1
0351
1
1
0352
3
1
0357
4
4
0358
3
4
0359
4
3
0360
4
4
0361
2
3
0362
2
3
0363
2
3
0364
4
3
0365
2
4
0366
1
1
0367
3
3
0368
3
3
0369
1
3
0370
2
4
den_
2000
(8)
3
2
3
4
4
2
1
2
2
2
2
2
1
3
1
2
3
1
2
1
3
4
4
3
2
3
2
road_
den
(17)
3
1
1
4
4
3
2
1
2
1
3
2
1
2
1
1
3
1
1
1
2
3
4
4
3
3
3
xing_
a
(17)
3
3
3
3
4
3
2
1
2
2
2
2
3
4
1
2
3
1
1
1
4
4
4
4
3
3
3
x1_
2_stm
(1)
4
3
3
4
4
4
2
2
3
2
3
2
3
3
1
2
3
1
2
1
4
4
4
4
3
2
3
b30_
rddn
(0.1)
4
4
4
4
4
4
4
3
3
3
4
3
3
4
3
3
4
3
3
4
4
4
4
4
3
3
4
no_
3_dep
(17)
1
2
2
1
1
2
4
4
4
4
4
4
4
3
4
4
4
4
4
4
4
4
4
4
4
4
4
res_
high
(2)
2
1
1
1
3
2
1
1
2
1
1
1
1
2
1
1
3
1
2
2
3
2
4
4
2
2
1
Mine
Human
(7)
3
1
3
1
1
1
1
1
1
1
4
1
1
1
1
4
4
4
4
4
4
4
4
4
4
1
4
(2)
3
3
3
3
4
4
1
3
3
2
3
3
1
2
1
1
2
1
1
1
1
2
1
4
2
3
2
Watershed
Scores
Category
219.4
189.4
269.4
252.4
338.4
274.4
222.4
188.3
213.3
203.3
249.4
222.3
211.3
248.4
175.3
234.3
300.4
184.3
195.3
186.4
287.4
324.4
355.4
411.4
348.3
336.3
322.4
low
very low
medium
low
high
medium
low
very low
low
low
low
low
low
low
very low
low
medium
very low
very low
very low
medium
high
high
very high
high
high
high
87
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0371
1
1
0372
2
1
0373
2
3
0374
3
1
0375
1
4
0376
1
3
0377
3
1
0378
2
1
0379
1
1
0380
2
1
0381
1
1
0382
2
1
0383
2
1
0384
2
1
0385
2
1
0386
3
1
0387
3
1
0388
1
1
0389
1
1
0390
1
1
0391
2
1
0392
3
1
0393
4
1
0396
4
3
0406
3
3
0407
3
3
0408
1
3
den_
2000
(8)
2
4
3
4
3
3
2
3
2
2
4
3
4
2
3
2
3
4
4
3
3
4
3
3
2
3
4
road_
den
(17)
2
3
1
4
2
3
1
2
2
1
3
3
3
3
3
3
3
4
2
3
3
2
3
3
2
1
2
xing_
a
(17)
4
4
3
4
4
4
4
4
4
4
4
4
2
4
3
4
3
4
3
4
4
4
2
2
2
1
1
x1_
2_stm
(1)
3
4
3
4
4
4
3
4
4
4
4
4
2
4
4
4
2
4
4
3
4
4
1
2
2
2
1
b30_
rddn
(0.1)
4
4
3
4
4
4
3
4
4
4
4
4
2
4
3
4
3
4
4
4
4
4
3
3
3
3
3
no_
3_dep
(17)
4
4
4
4
4
4
4
3
3
3
3
3
4
3
4
4
4
4
4
4
4
4
4
4
4
4
4
res_
high
(2)
3
4
1
4
2
2
2
2
2
2
3
1
4
1
4
1
1
4
1
2
1
1
2
2
2
1
3
Mine
Human
(7)
3
3
1
1
4
4
1
4
3
1
4
4
1
1
1
1
1
1
1
4
4
1
1
1
1
1
1
(2)
1
2
2
2
1
1
1
1
1
1
1
1
1
2
1
1
1
1
2
1
1
2
4
4
3
3
2
Watershed
Scores
Category
358.4
396.4
311.3
336.4
297.4
326.4
250.3
326.4
241.4
268.4
382.4
329.4
281.2
268.4
350.3
353.4
306.3
392.4
366.4
371.4
382.4
347.4
332.3
309.3
306.3
278.3
309.3
high
very high
high
high
medium
high
low
high
low
medium
very high
high
medium
medium
high
high
medium
very high
very high
very high
very high
high
high
high
medium
medium
high
88
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0409
2
4
0410
2
4
0411
4
3
0412
4
1
0413
3
1
0414
4
1
0415
3
1
0416
2
3
0417
1
1
0418
1
3
0419
2
4
0420
1
3
0421
4
1
0422
3
1
0423
4
3
0424
4
3
0425
1
3
0426
4
3
0427
4
4
0428
3
3
0429
4
3
0430
1
4
0431
4
3
0432
2
3
0433
4
3
0434
4
3
0435
2
4
den_
2000
(8)
4
3
3
3
3
3
2
4
4
4
4
2
2
4
4
3
3
4
3
4
2
3
2
3
4
2
3
road_
den
(17)
4
3
3
3
4
4
3
4
3
4
1
1
1
4
4
3
1
3
2
4
3
4
2
3
4
1
3
xing_
a
(17)
2
1
1
2
2
3
2
2
4
4
3
1
2
2
2
2
1
3
4
4
4
4
2
4
3
1
1
x1_
2_stm
(1)
3
1
3
3
3
4
4
2
4
4
2
2
3
2
2
1
4
3
4
4
4
4
2
3
3
1
1
b30_
rddn
(0.1)
3
1
3
2
4
4
4
3
4
4
3
1
3
2
2
2
1
2
4
3
4
4
3
4
3
1
1
no_
3_dep
(17)
4
4
4
4
4
4
4
4
4
3
3
3
3
3
3
3
3
3
3
3
3
4
3
3
3
3
3
res_
high
(2)
4
2
3
4
3
3
2
4
4
4
1
1
1
3
4
1
1
3
3
3
1
4
1
1
3
1
2
Mine
Human
(7)
4
1
4
4
1
1
1
1
3
4
1
1
4
1
1
1
1
1
1
4
4
4
4
1
1
1
1
(2)
4
4
4
4
4
4
4
4
1
2
2
1
1
2
2
1
1
2
2
3
1
3
1
1
3
1
1
Watershed
Scores
Category
401.3
303.1
328.3
323.2
329.4
359.4
303.4
391.3
331.4
391.4
265.3
237.1
264.3
356.2
370.2
295.2
223.1
357.2
333.4
403.3
345.4
414.4
263.3
307.4
388.3
212.1
256.1
very high
medium
high
high
high
high
medium
very high
high
very high
medium
low
medium
high
very high
medium
low
high
high
very high
high
very high
medium
high
very high
low
low
89
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0436
3
4
0437
3
3
0438
3
3
0439
1
3
0440
2
3
0441
3
3
0442
2
3
0443
4
4
0444
4
1
0445
1
4
0446
1
3
0447
3
3
0448
2
3
0449
3
4
0450
4
4
0451
3
3
0452
1
3
0453
3
4
0454
4
3
0455
2
4
0456
3
3
0457
2
4
0458
3
2
0459
1
3
0460
4
4
0461
1
3
0462
1
3
den_
2000
(8)
2
2
3
4
1
1
1
2
3
3
2
2
2
3
3
3
2
3
2
1
2
1
2
3
3
4
4
road_
den
(17)
2
3
3
4
2
1
1
3
3
4
4
3
3
3
3
4
2
2
1
1
1
1
2
2
3
4
4
xing_
a
(17)
1
1
2
3
3
2
2
2
1
2
1
2
2
3
2
3
2
3
3
2
2
2
3
3
4
4
4
x1_
2_stm
(1)
1
3
2
3
2
2
1
2
3
3
1
1
4
4
4
3
2
3
2
2
2
1
2
3
4
4
4
b30_
rddn
(0.1)
1
1
3
3
4
3
2
2
3
4
3
4
3
3
3
4
2
3
2
1
1
1
2
4
4
4
4
no_
3_dep
(17)
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
3
3
3
3
res_
high
(2)
1
1
2
2
1
2
3
3
2
2
1
1
2
2
2
2
3
4
1
3
2
1
2
2
3
3
4
Mine
Human
(7)
1
1
1
1
1
1
1
4
1
4
1
1
1
1
4
3
4
4
4
1
1
1
4
3
2
4
3
(2)
1
1
1
2
1
1
1
1
4
4
4
4
4
4
4
4
4
4
4
4
4
3
4
3
3
4
3
Watershed
Scores
Category
200.1
248.1
298.3
372.3
251.4
195.3
213.2
330.2
281.3
348.4
286.3
228.4
291.3
282.3
298.3
382.4
261.2
289.3
269.2
203.1
221.1
208.1
276.2
305.4
335.4
376.4
398.4
very low
low
medium
very high
low
very low
low
high
medium
high
medium
low
medium
medium
medium
very high
medium
medium
medium
low
low
low
medium
medium
high
very high
very high
90
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0463
1
2
0464
1
3
0465
3
3
0466
3
4
0467
3
2
0468
1
2
0469
1
2
0471
3
3
0472
1
3
0473
2
3
0474
1
3
0475
1
1
0476
1
3
0478
3
1
0479
4
1
0480
4
3
0481
3
1
0482
3
1
0485
4
1
0486
2
1
0487
3
1
0488
3
1
0492
3
1
0499
2
3
0500
2
3
0501
2
3
0502
2
4
den_
2000
(8)
4
4
3
4
4
4
4
4
4
4
3
4
2
4
3
4
2
2
4
4
3
4
4
4
4
4
3
road_
den
(17)
3
4
4
4
2
4
4
3
4
4
3
4
2
4
4
4
2
2
4
4
3
4
4
4
3
4
4
xing_
a
(17)
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
x1_
2_stm
(1)
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
b30_
rddn
(0.1)
4
4
4
4
4
4
4
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
no_
3_dep
(17)
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
res_
high
(2)
4
3
3
4
3
4
4
3
4
4
2
4
1
4
2
3
3
1
4
4
2
4
3
2
3
4
2
Mine
Human
(7)
4
3
1
1
1
3
3
1
4
4
1
4
1
4
1
1
1
1
3
1
1
1
1
1
1
1
1
(2)
3
2
2
1
2
4
2
3
3
3
3
3
1
3
2
2
1
1
3
2
1
3
2
2
1
3
1
Watershed
Scores
Category
376.4
418.4
396.4
392.4
334.4
412.4
420.4
389.3
429.4
429.4
355.4
429.4
283.4
429.4
365.4
392.4
229.4
237.4
386.4
394.4
334.4
396.4
404.4
402.4
385.4
372.4
327.4
very high
very high
very high
very high
high
very high
very high
very high
very high
very high
high
very high
medium
very high
very high
very high
low
low
very high
very high
high
very high
very high
very high
very high
very high
high
91
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0503
1
4
0504
4
4
0505
4
4
0506
3
4
0507
1
4
0508
3
4
0509
4
4
0510
4
4
0511
4
4
0512
4
4
0513
2
4
0514
4
4
0515
1
3
0516
4
4
0517
4
3
0518
3
4
0519
1
1
0520
2
1
0521
1
4
0522
3
4
0523
3
3
0524
3
4
0525
4
4
0526
4
4
0527
4
4
0528
1
4
0529
1
3
den_
2000
(8)
4
4
3
4
3
3
4
4
4
2
3
2
1
3
4
3
2
4
2
2
4
2
4
4
3
4
4
road_
den
(17)
4
4
2
3
2
4
4
4
4
2
4
2
1
4
4
2
1
3
2
1
4
2
3
4
4
3
4
xing_
a
(17)
4
4
4
4
4
4
4
4
4
3
4
4
1
4
4
3
2
4
3
3
4
4
4
4
4
4
4
x1_
2_stm
(1)
4
4
4
4
4
3
4
4
4
3
4
4
1
4
4
3
3
4
3
3
4
4
4
4
4
3
4
b30_
rddn
(0.1)
4
4
4
4
4
4
4
4
4
4
4
4
1
4
4
4
3
4
4
3
4
4
4
4
4
4
4
no_
3_dep
(17)
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
3
4
3
3
4
4
res_
high
(2)
3
4
3
4
1
1
2
2
3
1
2
1
1
2
3
2
3
4
2
1
2
2
3
3
2
3
4
Mine
Human
(7)
1
1
1
1
1
4
4
1
4
4
1
1
1
3
4
3
3
1
3
1
4
3
4
1
3
3
1
(2)
4
4
1
1
1
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
2
2
2
4
1
2
3
Watershed
Scores
Category
372.4
386.4
348.4
351.4
303.4
412.4
423.4
402.4
425.4
281.4
351.4
307.4
175.1
406.4
423.4
342.4
273.3
375.4
305.4
260.3
341.4
255.4
338.4
396.4
394.4
405.4
401.4
very high
very high
high
high
medium
very high
very high
very high
very high
medium
high
high
very low
very high
very high
high
medium
very high
medium
medium
high
low
high
very high
very high
very high
very high
92
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0530
1
4
0531
2
4
0533
3
4
0534
1
4
0535
2
3
0536
4
4
0537
4
4
0538
4
4
0539
4
4
0540
3
2
0541
3
3
0542
3
3
0543
1
2
0544
4
4
0545
4
4
0546
3
4
0547
3
3
0548
3
4
0549
3
3
0550
3
2
0551
3
1
0552
2
1
0553
4
1
0554
3
4
0555
3
4
0556
3
4
0557
2
4
den_
2000
(8)
4
3
4
4
3
4
2
3
4
4
3
3
1
2
3
1
2
1
1
2
2
1
1
1
1
3
1
road_
den
(17)
4
3
4
4
3
4
2
3
4
3
3
3
1
1
4
1
2
1
1
1
2
2
2
1
1
2
1
xing_
a
(17)
4
4
4
4
4
4
3
3
4
4
3
3
2
1
1
1
1
1
1
1
2
1
1
1
1
1
1
x1_
2_stm
(1)
4
4
4
4
4
4
3
2
4
3
2
3
2
2
1
1
1
1
1
1
1
1
1
1
1
3
1
b30_
rddn
(0.1)
4
4
4
4
4
4
3
4
4
3
2
2
3
1
3
1
2
1
1
1
2
2
1
1
1
2
1
no_
3_dep
(17)
3
3
3
3
3
3
3
4
3
3
3
3
4
4
1
1
1
1
1
1
1
2
2
1
1
1
2
res_
high
(2)
2
2
3
3
2
1
2
3
3
2
2
3
1
3
4
1
3
2
1
3
3
3
3
2
1
3
2
Mine
Human
(7)
1
1
4
4
1
4
1
1
4
1
1
4
1
1
3
3
4
2
2
4
4
3
3
3
1
4
3
(2)
2
2
2
2
1
2
1
2
2
4
4
4
1
1
2
2
3
1
2
2
2
1
2
1
1
2
1
Watershed
Scores
Category
402.4
365.4
425.4
425.4
339.4
409.4
291.3
336.4
413.4
352.3
321.2
345.2
210.3
234.1
300.3
157.1
236.2
174.1
162.1
217.1
251.2
217.2
207.1
181.1
165.1
256.2
198.1
very high
very high
very high
very high
high
very high
medium
high
very high
high
high
high
low
low
medium
very low
low
very low
very low
low
low
low
low
very low
very low
low
very low
93
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0558
4
4
0559
3
4
0560
4
4
0561
4
4
0562
1
4
0563
3
4
0564
3
3
0565
3
3
0566
3
4
0567
1
4
0568
3
3
0569
3
3
0570
1
2
0572
1
3
0573
2
4
0574
1
2
0575
2
3
0576
3
2
0577
2
2
0578
2
3
0579
2
3
0580
3
2
0581
2
2
0582
3
2
0583
3
2
0584
3
3
0585
3
2
den_
2000
(8)
2
2
2
2
4
4
4
4
4
1
1
1
3
1
4
3
2
4
2
3
3
2
3
4
3
1
1
road_
den
(17)
1
2
2
2
4
4
4
4
4
2
3
3
3
3
4
4
2
1
2
2
2
2
1
3
4
1
2
xing_
a
(17)
1
1
1
1
4
4
3
4
3
3
3
3
3
3
4
4
3
1
3
4
3
2
1
4
4
3
3
x1_
2_stm
(1)
1
1
1
4
2
4
3
4
2
2
3
2
3
3
4
4
3
4
3
3
1
2
1
4
4
3
3
b30_
rddn
(0.1)
1
2
2
2
3
4
3
3
3
2
3
3
3
3
3
4
3
2
3
2
1
1
1
3
4
3
4
no_
3_dep
(17)
1
2
1
1
4
4
4
4
4
3
3
4
3
3
4
4
4
4
4
1
1
1
3
4
4
4
4
res_
high
(2)
3
3
2
1
4
4
4
4
4
1
2
2
3
1
4
3
1
3
2
3
2
3
2
4
4
1
2
Mine
Human
(7)
3
4
2
2
1
1
1
1
1
3
2
3
3
4
4
3
3
1
3
4
1
1
1
3
4
3
4
(2)
2
3
1
2
4
4
4
4
4
3
2
1
3
1
4
2
4
3
2
4
3
3
2
4
4
2
2
Watershed
Scores
Category
222.1
253.2
199.2
202.2
401.3
391.4
397.3
391.3
372.3
281.2
292.3
313.3
348.3
302.3
448.3
427.4
338.3
198.2
324.3
323.2
279.1
257.1
166.1
424.3
411.4
268.3
335.4
low
low
very low
low
very high
very high
very high
very high
very high
medium
medium
high
high
medium
very high
very high
high
very low
high
high
medium
low
very low
very high
very high
medium
high
94
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0586
3
3
0587
2
3
0588
3
2
0589
3
2
0590
2
4
0591
1
4
0592
3
4
0593
1
4
0594
1
4
0595
4
2
0596
4
2
0597
4
2
0598
4
3
0599
4
2
0600
4
4
0601
4
4
0602
4
3
0603
1
1
0611
3
3
0614
4
3
0615
4
3
0616
4
3
0617
1
1
0618
4
4
0619
4
3
0620
3
2
0621
4
3
den_
2000
(8)
2
3
2
3
4
3
4
3
3
2
4
2
2
2
2
2
4
2
4
2
2
3
1
1
1
1
2
road_
den
(17)
2
3
2
1
3
3
3
4
2
1
3
2
2
1
3
2
4
3
2
3
2
3
1
1
1
1
2
xing_
a
(17)
3
4
2
2
4
3
4
4
4
3
3
2
2
2
3
3
3
3
3
3
2
3
2
1
1
2
4
x1_
2_stm
(1)
3
3
1
4
3
2
3
2
3
2
2
2
1
2
2
1
2
2
2
2
1
2
2
1
1
1
3
b30_
rddn
(0.1)
3
3
2
2
3
2
1
2
2
1
2
1
1
1
2
1
2
2
2
2
2
2
2
2
1
2
3
no_
3_dep
(17)
4
4
3
3
3
3
2
3
3
3
1
2
2
2
3
3
3
3
2
3
3
3
1
1
1
1
1
res_
high
(2)
2
4
2
4
4
4
3
1
3
1
4
3
2
3
4
2
4
2
3
3
1
4
2
1
1
1
3
Mine
Human
(7)
4
3
1
1
1
1
3
1
1
1
4
1
1
1
4
1
4
1
4
1
1
3
1
1
1
4
3
(2)
2
4
3
4
4
4
4
4
4
3
4
3
3
3
4
4
4
3
4
4
3
4
4
4
4
4
3
Watershed
Scores
Category
331.3
328.3
194.2
194.2
281.3
243.2
387.1
394.2
336.2
287.1
332.2
274.1
213.1
257.1
292.2
307.1
412.2
253.2
359.2
269.2
274.2
351.2
162.2
118.2
142.1
168.2
306.3
high
high
very low
very low
medium
low
very high
very high
high
medium
high
medium
low
low
medium
medium
very high
low
high
medium
medium
high
very low
very low
very low
very low
medium
95
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0622
3
3
0623
4
1
0624
1
1
0625
1
1
0626
2
1
0627
1
1
0628
4
4
0629
4
4
0630
4
3
0631
4
3
0632
4
3
0633
4
3
0634
4
1
0635
4
3
0636
4
1
0637
4
3
0638
4
4
0639
3
1
0640
4
4
0641
4
1
0642
3
3
0643
4
3
0644
3
1
0645
1
1
0646
3
1
0647
2
1
0648
4
3
den_
2000
(8)
2
1
1
2
2
2
1
1
4
2
3
2
3
1
1
1
1
3
2
1
3
2
3
2
2
3
1
road_
den
(17)
1
1
1
2
1
2
2
1
4
1
2
1
2
1
1
1
2
4
1
1
4
4
4
3
3
4
2
xing_
a
(17)
3
3
2
3
3
2
3
3
4
3
3
3
3
3
3
3
4
4
3
2
2
2
2
1
2
2
2
x1_
2_stm
(1)
3
3
3
4
2
1
3
3
4
2
3
2
2
3
3
2
3
4
2
3
2
2
2
2
4
4
1
b30_
rddn
(0.1)
2
3
2
3
2
3
3
3
4
2
2
3
2
3
3
2
3
3
2
1
2
2
2
1
1
2
1
no_
3_dep
(17)
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
res_
high
(2)
1
2
1
1
3
2
1
1
3
1
2
2
3
1
1
1
1
3
1
1
4
3
3
1
1
4
2
Mine
Human
(7)
1
3
1
3
3
1
1
1
3
1
1
3
3
4
1
1
1
1
1
1
3
3
2
1
3
4
1
(2)
2
3
2
1
2
1
2
1
3
2
3
2
3
1
1
1
2
1
1
1
2
2
2
1
2
2
1
Watershed
Scores
Category
240.2
226.3
162.2
217.3
257.2
238.3
196.3
177.3
386.4
227.2
286.2
231.3
272.2
222.3
177.3
200.2
213.3
360.3
220.2
155.1
301.2
279.2
234.2
155.1
260.1
286.2
201.1
low
low
very low
low
medium
low
very low
very low
very high
low
medium
low
medium
low
very low
very low
low
very high
low
very low
medium
medium
low
very low
medium
medium
low
96
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0649
3
3
0650
1
3
0651
1
2
0652
1
2
0653
3
3
0654
3
3
0655
1
2
0656
1
2
0657
4
4
0658
2
3
0659
2
4
0660
1
3
0661
2
3
0662
3
2
0663
1
2
0664
3
2
0665
1
2
0666
4
4
0667
4
2
0668
3
1
0669
3
3
0670
2
3
0671
3
1
0672
1
1
0673
2
3
0674
1
3
0675
3
2
den_
2000
(8)
3
3
2
1
3
2
2
1
2
1
1
2
2
2
3
4
2
1
3
3
1
4
1
1
1
1
1
road_
den
(17)
3
1
1
1
2
2
1
1
2
1
1
1
2
2
2
3
2
2
2
3
2
4
1
1
1
1
1
xing_
a
(17)
2
3
2
2
2
2
2
2
2
2
2
2
3
2
2
4
2
2
1
2
1
2
1
2
1
1
1
x1_
2_stm
(1)
3
2
1
1
1
1
1
1
1
1
1
1
3
2
1
1
3
1
1
3
3
4
1
1
1
1
1
b30_
rddn
(0.1)
2
1
1
1
1
1
1
1
1
1
1
1
1
1
3
3
1
1
1
2
1
2
2
1
1
1
1
no_
3_dep
(17)
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
res_
high
(2)
3
3
3
1
1
3
2
1
2
3
1
2
2
3
2
4
3
1
4
4
3
4
3
1
3
1
1
Mine
Human
(7)
4
3
1
1
3
1
1
1
1
1
4
1
1
1
1
4
4
3
2
4
2
4
3
2
1
1
1
(2)
2
3
3
3
3
3
3
2
1
1
3
2
4
4
4
4
2
2
3
2
3
4
3
4
4
1
1
Watershed
Scores
Category
290.2
209.1
239.1
157.1
262.1
198.1
167.1
131.1
168.1
157.1
190.1
165.1
205.1
201.1
194.3
266.3
183.1
174.1
232.1
222.2
146.1
264.2
163.2
171.1
163.1
141.1
141.1
medium
low
low
very low
medium
very low
very low
very low
very low
very low
very low
very low
low
low
very low
medium
very low
very low
low
low
very low
medium
very low
very low
very low
very low
very low
97
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0676
3
3
0677
3
1
0678
4
3
0679
3
1
0680
3
3
0681
4
1
0682
3
1
0683
1
1
0684
2
1
0685
3
1
0686
4
1
0687
3
1
0688
3
1
0689
4
1
0690
3
1
0691
2
1
0692
1
1
0693
2
1
0694
2
3
0695
2
1
0696
1
1
0697
3
1
0698
1
2
0699
1
2
0700
2
2
0701
1
2
0702
1
2
den_
2000
(8)
1
1
1
1
1
1
2
2
1
1
1
3
1
1
1
1
1
1
1
2
1
3
1
1
1
1
1
road_
den
(17)
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
2
2
2
1
1
1
1
1
xing_
a
(17)
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
1
1
1
1
1
x1_
2_stm
(1)
1
1
1
1
1
2
1
3
1
1
1
1
1
2
2
1
1
1
2
3
3
3
1
3
1
1
1
b30_
rddn
(0.1)
1
1
1
1
1
2
1
1
1
1
1
2
2
1
1
1
1
1
1
3
3
2
2
1
1
1
2
no_
3_dep
(17)
1
2
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
res_
high
(2)
1
1
1
1
1
2
2
4
1
1
1
2
3
1
1
1
1
1
1
1
3
1
1
1
2
1
1
Mine
Human
(7)
1
2
1
2
1
4
2
2
1
1
3
4
3
3
4
2
4
2
3
4
4
4
4
3
3
4
3
(2)
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
1
2
1
1
Watershed
Scores
Category
141.1
165.1
141.1
148.1
158.1
148.2
141.1
147.1
141.1
141.1
155.1
192.2
130.2
156.1
146.1
172.1
162.1
119.1
129.1
179.3
175.3
211.2
135.2
128.1
183.1
174.1
179.2
very low
very low
very low
very low
very low
very low
very low
very low
very low
very low
very low
very low
very low
very low
very low
very low
very low
very low
very low
very low
very low
low
very low
very low
very low
very low
very low
98
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0703
1
2
0704
1
2
0705
1
2
0706
1
2
0707
1
2
0708
2
1
0709
2
1
0710
2
1
0711
1
2
0712
1
2
0713
1
2
0714
3
1
0715
1
1
0716
1
2
0717
2
1
0718
3
2
0719
1
2
0720
1
1
0721
1
1
0722
1
1
0723
1
1
0724
3
1
0725
1
1
0726
1
1
0727
3
2
0728
2
2
0729
3
2
den_
2000
(8)
1
1
1
1
2
3
1
3
2
2
4
3
3
4
1
3
3
4
2
3
1
3
4
3
3
3
2
road_
den
(17)
1
1
1
1
3
3
2
3
3
3
4
4
3
3
1
3
4
4
2
3
2
2
4
3
3
3
3
xing_
a
(17)
1
1
1
1
3
3
3
3
2
3
4
4
4
4
3
3
3
4
2
2
2
3
2
4
1
3
3
x1_
2_stm
(1)
1
1
1
4
3
3
2
2
2
2
3
2
3
3
3
4
3
4
2
2
2
3
3
3
1
2
3
b30_
rddn
(0.1)
2
2
1
1
3
2
2
3
2
3
3
3
3
3
3
3
4
4
3
2
2
3
2
3
1
3
3
no_
3_dep
(17)
1
1
1
1
3
3
3
3
3
3
3
3
1
1
3
3
3
3
3
3
3
2
2
3
2
2
1
res_
high
(2)
2
2
1
1
2
3
1
4
3
3
4
3
3
4
3
3
1
4
3
4
2
4
4
4
4
3
3
Mine
Human
(7)
3
3
3
2
3
3
3
4
1
1
4
3
4
3
1
3
1
4
3
3
3
3
3
3
4
3
3
(2)
1
1
2
3
4
4
4
3
3
4
2
3
4
4
2
3
4
4
4
3
4
4
3
4
2
3
4
Watershed
Scores
Category
169.2
181.2
157.1
126.1
328.3
338.2
259.2
332.3
272.2
291.3
361.3
369.3
369.3
372.3
229.3
354.3
342.4
419.4
283.3
337.2
273.2
323.3
346.2
362.3
295.1
323.3
284.3
very low
very low
very low
very low
high
high
medium
high
medium
medium
very high
very high
very high
very high
low
high
high
very high
medium
high
medium
high
high
very high
medium
high
medium
99
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0730
2
2
0731
3
2
0732
1
2
0733
1
1
0734
3
3
0735
3
3
0736
2
2
0737
2
3
0738
1
3
0739
1
3
0740
1
3
0741
3
3
0742
4
4
0743
4
4
0744
2
2
0745
2
4
0746
1
4
0747
3
4
0748
2
3
0749
2
4
0750
2
3
0751
2
4
0752
2
4
0753
1
4
0754
1
4
0755
1
4
0756
2
3
den_
2000
(8)
4
2
2
3
2
2
1
2
1
1
2
2
1
1
1
1
1
3
3
3
1
2
1
2
1
3
2
road_
den
(17)
4
3
2
3
2
2
1
1
1
1
2
2
2
2
1
2
2
2
3
3
1
2
2
3
1
3
2
xing_
a
(17)
2
2
2
3
2
2
1
2
2
2
3
3
3
3
3
4
4
3
4
4
2
4
4
4
2
3
3
x1_
2_stm
(1)
2
2
2
3
1
2
1
2
2
2
2
3
3
3
3
4
3
3
3
2
2
3
3
2
2
3
2
b30_
rddn
(0.1)
2
2
2
3
2
2
1
1
1
2
2
2
4
3
3
3
3
3
3
3
2
3
3
3
2
2
2
no_
3_dep
(17)
2
1
1
1
1
1
2
1
1
1
2
2
4
4
4
4
4
4
4
4
4
3
4
4
4
4
4
res_
high
(2)
4
3
3
4
3
4
2
3
2
2
1
2
1
2
1
1
3
3
4
4
2
3
2
2
1
3
3
Mine
Human
(7)
4
1
3
3
1
2
3
3
3
2
3
3
3
2
2
1
3
4
2
1
1
3
1
3
1
1
2
(2)
3
3
3
3
2
3
2
2
2
3
4
4
1
1
1
2
2
2
4
4
1
3
1
2
1
3
4
Watershed
Scores
Category
364.2
209.2
247.2
309.3
218.2
242.2
188.1
228.1
218.1
201.2
267.2
282.2
295.4
319.3
288.3
265.3
323.3
341.3
367.3
347.3
236.2
304.3
288.3
345.3
210.2
339.2
334.2
very high
low
low
high
low
low
very low
low
low
low
medium
medium
medium
high
medium
medium
high
high
very high
high
low
medium
medium
high
low
high
high
100
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0757
3
4
0758
1
2
0759
2
3
0760
1
4
0761
1
3
0762
2
3
0763
2
2
0764
2
3
0765
2
3
0766
1
3
0767
1
3
0768
2
3
0769
4
2
0770
4
3
0771
3
3
0772
1
2
0773
2
3
0774
3
3
0775
3
3
0776
2
3
0777
3
2
0778
1
3
0779
3
2
0780
2
3
0781
1
2
0782
3
3
0783
4
3
den_
2000
(8)
2
2
1
2
1
1
4
4
4
4
3
2
3
3
2
1
1
2
3
3
1
3
3
3
1
3
3
road_
den
(17)
1
2
2
1
1
1
4
4
4
4
3
2
2
3
2
1
1
2
3
4
2
3
3
2
1
3
3
xing_
a
(17)
3
4
3
3
1
1
4
4
4
3
4
2
3
4
4
2
3
3
4
4
1
4
3
3
2
3
3
x1_
2_stm
(1)
3
3
3
3
1
1
4
4
3
3
3
1
2
3
2
2
2
3
4
3
1
3
3
2
2
3
2
b30_
rddn
(0.1)
2
2
2
1
1
1
4
4
4
3
3
2
2
3
3
2
2
2
3
3
2
3
3
2
2
2
2
no_
3_dep
(17)
4
3
4
3
1
1
3
3
3
3
3
3
3
2
2
2
3
3
3
3
3
3
3
3
2
3
2
res_
high
(2)
2
3
2
2
1
1
4
4
4
4
3
1
3
3
3
1
1
1
4
3
2
3
4
2
1
2
4
Mine
Human
(7)
1
1
2
1
1
3
1
3
1
3
1
1
2
1
4
1
1
1
4
4
1
1
2
4
3
3
1
(2)
3
4
3
4
1
2
4
4
4
4
4
2
3
4
3
2
2
3
4
4
2
4
4
4
1
4
4
Watershed
Scores
Category
307.2
304.2
241.2
222.1
141.1
157.1
386.4
412.4
409.4
382.3
358.3
231.2
328.2
336.3
317.3
190.2
236.2
293.2
341.3
408.3
232.2
370.3
362.3
313.2
190.2
348.2
308.2
medium
medium
low
low
very low
very low
very high
very high
very high
very high
high
low
high
high
high
very low
low
medium
high
very high
low
very high
very high
high
very low
high
high
101
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0784
4
3
0785
2
3
0786
2
1
0787
3
1
0788
1
2
0789
1
2
0790
2
4
0791
3
4
0792
4
4
0793
2
4
0794
2
4
0795
2
2
0796
2
4
0797
4
3
0798
3
3
0799
2
2
0800
3
2
0801
3
3
0802
1
3
0803
3
4
0804
4
2
0805
3
4
0806
3
4
0807
2
3
0808
1
2
0809
4
3
0810
3
3
den_
2000
(8)
4
3
3
2
1
1
1
1
1
4
4
2
1
2
2
2
2
2
1
2
1
1
2
2
1
2
2
road_
den
(17)
4
2
3
2
1
2
1
2
1
3
4
3
2
2
2
1
2
1
2
2
1
1
2
2
2
2
1
xing_
a
(17)
4
3
4
3
1
3
2
1
1
4
4
4
3
3
2
3
3
3
3
3
3
3
3
3
3
3
2
x1_
2_stm
(1)
4
3
4
3
1
3
2
1
1
3
4
3
2
3
2
2
3
4
3
3
3
2
2
3
3
2
2
b30_
rddn
(0.1)
3
2
3
2
1
2
2
1
1
3
3
3
2
2
3
2
2
2
3
2
2
2
2
3
3
3
1
no_
3_dep
(17)
2
3
2
1
2
1
1
1
1
1
1
1
1
2
2
1
1
1
1
1
1
1
1
1
1
1
1
res_
high
(2)
4
2
3
2
2
1
1
1
1
3
4
2
1
3
2
2
2
3
2
3
1
1
2
2
1
1
2
Mine
Human
(7)
4
3
3
3
2
4
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
(2)
4
4
4
3
1
2
2
1
1
4
4
4
3
4
3
3
4
3
3
4
2
2
2
3
1
1
4
Watershed
Scores
Category
390.3
348.2
368.3
263.2
167.1
246.2
161.2
158.1
141.1
344.3
359.3
309.3
221.2
282.2
248.3
243.2
275.2
235.2
241.3
277.2
179.2
178.2
234.2
302.3
218.3
254.3
182.1
very high
high
very high
medium
very low
low
very low
very low
very low
high
high
high
low
medium
low
low
medium
low
low
medium
very low
very low
low
medium
low
low
very low
102
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0811
2
4
0812
3
4
0813
3
4
0814
2
3
0815
1
2
0816
1
3
0817
1
2
0818
1
2
0819
1
2
0820
3
4
0821
2
4
0822
4
3
0823
3
2
0824
3
3
0825
2
3
0826
3
3
0827
4
3
0828
2
3
0829
2
3
0830
4
3
0831
1
2
0832
1
2
0833
1
3
0834
4
4
0835
3
2
0836
3
3
0837
4
1
den_
2000
(8)
2
1
2
2
1
3
2
1
1
1
2
1
2
4
2
4
3
2
2
2
2
1
2
2
2
2
3
road_
den
(17)
1
1
1
1
2
3
2
2
2
3
3
3
3
4
3
4
3
3
3
1
3
3
3
3
3
2
3
xing_
a
(17)
1
3
3
3
3
3
4
2
2
2
3
2
2
2
1
2
2
1
2
3
2
1
2
1
1
2
2
x1_
2_stm
(1)
1
2
3
2
1
3
3
2
2
2
2
3
3
2
1
1
3
1
1
1
1
2
1
3
1
1
1
b30_
rddn
(0.1)
1
1
2
2
2
2
2
2
3
3
2
2
1
2
3
3
1
1
4
1
1
1
4
1
1
1
1
no_
3_dep
(17)
1
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
res_
high
(2)
1
1
3
2
1
4
2
1
2
1
2
1
3
4
4
4
3
3
1
2
3
1
3
3
1
3
4
Mine
Human
(7)
1
1
3
1
1
4
1
3
3
3
4
1
1
4
1
1
4
2
1
3
1
1
1
4
1
3
3
(2)
4
2
3
4
4
4
3
2
2
1
3
2
2
3
3
3
3
2
4
1
2
2
4
2
2
4
4
Watershed
Scores
Category
150.1
207.1
289.2
257.2
198.2
313.2
278.2
192.2
194.3
207.3
216.2
237.2
191.1
272.2
222.3
284.3
280.1
237.1
247.4
182.1
189.1
178.1
263.4
195.1
226.1
214.1
316.1
very low
low
medium
medium
very low
high
medium
very low
very low
low
low
low
very low
medium
low
medium
medium
low
low
very low
very low
very low
medium
very low
low
low
high
103
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0838
3
1
0839
2
2
0840
4
3
0841
4
3
0842
1
2
0844
3
3
0845
3
3
0846
1
2
0847
1
2
0849
1
2
0850
1
1
0853
2
3
0854
2
1
0855
4
1
0856
1
3
0857
2
3
0858
2
3
0859
2
3
0860
2
3
0861
2
1
0862
2
1
0863
1
2
0864
3
3
0865
2
1
0866
2
3
0867
4
1
0868
3
4
den_
2000
(8)
3
3
2
2
1
3
3
2
2
1
4
2
4
1
2
1
4
2
4
2
1
2
4
2
3
4
2
road_
den
(17)
3
3
1
2
2
3
2
2
2
1
3
1
4
2
1
2
2
1
3
2
1
2
4
3
4
4
2
xing_
a
(17)
2
2
1
1
2
2
3
2
2
2
1
1
1
2
1
2
1
1
2
1
1
2
1
2
3
3
2
x1_
2_stm
(1)
1
1
1
2
1
2
3
2
2
1
3
3
2
2
1
1
2
1
3
1
4
2
3
3
3
3
3
b30_
rddn
(0.1)
2
1
1
1
1
1
2
1
1
1
2
1
1
1
1
1
1
1
2
1
1
1
2
2
3
2
1
no_
3_dep
(17)
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
res_
high
(2)
3
4
2
2
4
3
4
3
3
2
2
1
4
1
3
1
3
2
4
3
3
4
4
4
3
4
4
Mine
Human
(7)
1
2
1
1
3
1
4
1
1
1
4
1
3
4
4
3
3
3
1
1
1
3
1
4
1
1
3
(2)
2
4
2
3
2
3
4
4
4
3
3
3
4
1
3
2
4
3
4
3
1
3
3
2
4
4
3
Watershed
Scores
Category
267.2
309.1
182.1
214.1
250.1
270.1
308.2
247.1
235.1
176.1
310.2
184.1
313.1
209.1
190.1
191.1
272.1
210.1
312.2
215.1
177.1
261.1
310.2
284.2
348.3
358.2
250.1
medium
high
very low
low
low
medium
high
low
low
very low
high
very low
high
low
very low
very low
medium
low
high
low
very low
medium
high
medium
high
high
low
104
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0869
3
3
0870
3
4
0871
1
3
0872
2
3
0873
3
3
0874
3
3
0875
4
3
0876
3
3
0877
2
3
0878
2
2
0879
3
4
0880
1
3
0881
2
4
0882
2
2
0883
2
2
0884
1
2
0885
4
3
0886
2
3
0887
3
4
0888
2
3
0889
1
3
0890
3
3
0891
3
4
0892
3
3
0893
4
4
0894
4
4
0895
2
3
den_
2000
(8)
1
2
1
4
2
3
2
1
1
1
2
1
1
1
3
1
1
1
2
3
1
1
2
1
1
2
1
road_
den
(17)
1
1
2
4
3
3
3
3
2
2
2
2
1
1
3
3
3
2
3
3
2
2
2
3
1
3
1
xing_
a
(17)
1
2
2
3
3
3
2
2
2
1
1
2
1
1
4
2
3
3
4
4
1
3
2
1
1
1
2
x1_
2_stm
(1)
1
3
1
3
2
3
2
3
1
3
1
2
1
3
4
3
3
2
3
4
2
3
3
4
1
4
4
b30_
rddn
(0.1)
1
1
1
3
3
3
3
3
2
2
2
1
1
1
3
3
2
2
3
3
2
3
3
3
1
3
2
no_
3_dep
(17)
1
1
1
4
3
3
2
3
2
2
1
1
1
1
3
3
2
2
2
2
2
2
1
2
3
3
3
res_
high
(2)
1
4
1
3
1
4
3
1
2
2
3
3
2
2
3
2
3
2
4
4
2
2
3
2
2
4
2
Mine
Human
(7)
1
1
1
3
4
4
3
1
1
1
1
2
2
1
3
1
3
2
3
2
2
2
1
3
1
2
3
(2)
4
4
3
3
4
4
2
2
2
2
1
1
1
1
4
3
3
2
2
4
2
3
1
2
1
2
4
Watershed
Scores
Category
159.1
233.1
244.1
419.3
308.3
347.3
279.3
242.3
220.2
181.2
199.2
187.1
162.1
145.1
356.3
258.3
291.2
221.2
304.3
252.3
228.2
207.3
172.3
184.3
177.1
276.3
270.2
very low
low
low
very high
high
high
medium
low
low
very low
very low
very low
very low
very low
high
medium
medium
low
medium
low
low
low
very low
very low
very low
medium
medium
105
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0896
2
2
0897
3
3
0899
4
3
0900
4
4
0901
1
3
0902
3
3
0903
2
3
0904
2
2
0905
3
2
0906
1
2
0907
1
3
0908
1
2
0909
2
2
0910
1
2
0911
3
3
0912
3
2
0913
2
3
0914
1
2
0915
1
3
0916
1
1
0917
2
3
0918
2
1
0919
2
1
0920
1
1
0921
1
2
0922
2
3
0923
3
3
den_
2000
(8)
1
1
2
1
1
1
2
1
1
1
1
1
1
3
2
1
1
2
4
3
4
2
3
4
4
4
1
road_
den
(17)
1
2
2
2
1
2
1
1
2
2
2
1
2
3
4
1
2
2
4
3
2
1
3
4
4
3
1
xing_
a
(17)
2
2
1
1
1
1
1
2
1
1
1
2
1
1
1
1
1
1
3
2
2
2
2
3
2
2
1
x1_
2_stm
(1)
2
3
2
1
1
1
1
2
1
3
2
3
1
1
1
2
3
2
3
2
2
2
3
3
3
3
1
b30_
rddn
(0.1)
1
2
2
2
1
1
1
2
2
1
1
2
1
2
1
2
2
2
2
1
1
1
2
3
2
2
1
no_
3_dep
(17)
3
2
2
1
2
1
3
3
2
3
2
2
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
res_
high
(2)
2
3
3
2
1
2
1
2
2
2
1
1
1
4
4
2
2
4
4
4
4
2
4
4
4
4
1
Mine
Human
(7)
1
2
2
1
1
3
1
2
1
1
1
1
3
3
1
1
1
2
3
1
1
1
4
4
4
3
1
(2)
3
2
3
1
1
1
2
2
1
1
1
1
1
2
1
2
2
2
4
4
4
4
4
4
4
3
2
Watershed
Scores
Category
211.1
248.2
240.2
160.2
129.1
157.1
168.1
216.2
160.2
196.1
188.1
189.2
172.1
254.2
230.1
163.2
147.2
223.2
372.2
303.1
294.1
228.1
337.2
379.3
362.2
300.2
143.1
low
low
low
very low
very low
very low
very low
low
very low
very low
very low
very low
very low
low
low
very low
very low
low
very high
medium
medium
low
high
very high
very high
medium
very low
106
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0924
2
2
0925
2
3
0926
2
3
0927
1
2
0928
1
1
0929
2
1
0930
2
1
0931
2
2
0932
2
1
0933
1
2
0934
2
2
0935
2
2
0936
1
2
0937
2
3
0938
3
2
0939
1
2
0940
2
1
0941
2
3
0950
4
4
0951
3
4
0952
3
4
0953
3
3
0954
4
4
0955
4
4
0956
4
4
0957
1
4
0960
1
2
den_
2000
(8)
1
1
1
1
3
3
4
4
4
2
3
4
2
2
1
1
1
1
4
3
1
1
2
2
3
4
2
road_
den
(17)
1
1
3
1
3
2
4
4
4
2
3
4
3
1
1
2
1
1
4
2
1
2
3
2
4
4
3
xing_
a
(17)
1
1
1
1
1
1
1
3
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
x1_
2_stm
(1)
1
4
1
3
3
4
1
4
1
2
1
4
1
1
1
1
1
2
1
1
1
1
3
1
1
1
4
b30_
rddn
(0.1)
1
1
3
1
2
2
3
3
4
1
2
1
1
1
1
1
1
1
2
2
1
2
1
1
1
3
3
no_
3_dep
(17)
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
4
3
2
2
2
4
3
3
4
1
1
1
res_
high
(2)
1
2
1
1
4
3
4
4
4
2
2
4
2
1
1
1
1
1
4
3
1
3
2
3
4
4
4
Mine
Human
(7)
1
1
4
1
4
1
1
1
3
3
4
1
1
1
1
1
1
1
1
1
1
1
1
1
3
1
1
(2)
1
2
2
1
2
3
3
4
4
3
3
3
3
1
1
2
1
1
2
1
1
1
2
1
3
3
1
Watershed
Scores
Category
141.1
148.1
234.3
155.1
280.2
243.2
284.3
347.3
300.4
199.1
288.2
287.1
252.1
178.1
141.1
211.1
175.1
171.1
299.2
253.2
192.1
196.2
223.1
221.1
290.1
284.3
221.3
very low
very low
low
very low
medium
low
medium
high
medium
very low
medium
medium
low
very low
very low
low
very low
very low
medium
low
very low
very low
low
low
medium
medium
low
107
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0961
1
2
0962
1
2
0963
4
2
0964
2
2
0965
1
4
0966
1
4
0967
1
4
0968
3
4
0969
1
4
0970
2
2
0971
2
4
0972
1
4
0973
1
3
0974
1
3
0975
1
2
0976
1
2
0977
1
2
0978
2
2
0979
1
4
0980
1
4
0981
1
2
0982
1
2
0983
1
2
0984
1
2
0985
1
4
0986
1
4
0987
1
3
den_
2000
(8)
2
4
1
4
4
4
4
2
2
4
3
2
2
2
4
4
4
4
3
3
3
4
3
4
3
3
3
road_
den
(17)
3
3
1
4
4
3
4
2
4
4
4
2
4
2
3
4
4
4
4
3
4
4
4
4
4
3
4
xing_
a
(17)
1
1
1
1
1
1
1
1
1
1
1
3
2
1
2
4
3
4
3
1
3
2
4
4
4
3
4
x1_
2_stm
(1)
1
4
1
1
4
2
1
1
1
1
1
1
3
1
1
1
1
4
1
2
2
1
3
4
4
3
4
b30_
rddn
(0.1)
4
2
1
1
2
1
1
1
2
4
4
1
3
1
1
3
2
3
4
1
2
3
3
4
4
4
4
no_
3_dep
(17)
1
1
1
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
3
3
2
1
4
4
4
3
4
res_
high
(2)
4
4
1
4
4
4
4
2
3
4
4
4
3
3
4
4
4
4
1
4
4
4
3
2
4
2
3
Mine
Human
(7)
1
1
1
3
1
3
4
3
4
4
2
2
3
1
2
4
2
3
3
4
1
2
3
1
1
2
4
(2)
3
3
2
3
4
4
4
3
2
4
3
1
2
1
2
3
3
3
1
3
3
2
4
4
4
4
4
Watershed
Scores
Category
193.4
270.2
143.1
315.1
318.2
301.1
336.1
268.1
239.2
343.4
278.4
213.1
263.3
170.1
272.1
392.3
337.2
352.3
355.4
351.1
335.2
318.3
307.3
382.4
354.4
247.4
397.4
very low
medium
very low
high
high
medium
high
medium
low
high
medium
low
medium
very low
medium
very high
high
high
high
high
high
high
high
very high
high
low
very high
108
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
0988
1
2
0989
1
4
0990
1
2
0991
1
4
0992
2
4
0993
1
4
0994
2
4
1000
4
3
1001
2
2
1002
4
4
1003
3
3
1004
1
2
1005
3
2
1006
1
2
1007
2
3
1008
4
4
1009
2
4
1010
1
4
1011
4
3
1012
4
4
1013
4
3
1014
2
4
1015
1
1
1016
3
3
1017
1
3
1018
1
1
1018
3
3
den_
2000
(8)
4
4
4
3
3
3
4
4
3
2
3
4
3
2
4
4
4
4
2
4
4
3
3
4
2
2
4
road_
den
(17)
4
4
2
4
3
3
4
4
3
2
4
4
4
4
4
4
4
4
4
4
4
4
4
4
3
4
4
xing_
a
(17)
4
4
4
4
4
2
3
2
3
1
4
4
4
4
4
4
4
4
4
4
4
4
3
4
4
4
4
x1_
2_stm
(1)
4
4
4
4
3
3
3
4
2
1
3
3
4
4
4
4
4
4
3
4
4
4
2
4
4
3
3
b30_
rddn
(0.1)
4
4
4
4
4
3
4
3
3
1
4
3
4
4
4
4
4
4
4
4
4
4
3
4
4
4
4
no_
3_dep
(17)
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
res_
high
(2)
2
1
2
2
3
3
2
4
3
1
2
4
2
3
3
4
4
3
2
4
4
2
3
4
2
2
4
Mine
Human
(7)
4
4
4
3
4
4
3
1
1
1
3
3
1
4
4
1
2
4
4
4
3
3
1
4
4
1
3
(2)
4
3
4
3
3
4
4
4
4
3
4
4
3
3
4
4
4
3
3
3
3
4
4
4
4
3
4
Watershed
Scores
Category
316.4
324.4
369.4
287.4
389.4
362.3
419.4
270.3
316.3
250.1
375.4
440.3
401.4
387.4
405.4
328.4
422.4
345.4
384.4
381.4
398.4
400.4
311.3
448.4
370.4
293.4
440.4
high
high
very high
medium
very high
very high
very high
medium
high
low
very high
very high
very high
very high
very high
high
very high
high
very high
very high
very high
very high
high
very high
very high
medium
very high
109
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
1019
2
1
1023
3
1
1024
2
4
1026
1
1
1027
3
4
1027
2
4
1028
4
4
1029
1
1
1032
1
3
1033
1
3
1049
2
3
1050
4
4
1052
3
4
1053
3
3
1054
3
3
1055
3
1
1056
3
4
1057
3
1
1058
3
3
1059
1
3
1060
3
3
1061
4
3
1062
4
1
1064
4
4
1065
3
3
1067
2
1
1069
4
4
Table A9. continued.
WS
dam_ pop_
cat
ID
a
(29)
(12)
1070
2
3
1071
3
3
1072
4
3
1073
4
4
1074
4
1
1075
4
4
1076
4
1
1077
3
1
1079
1
4
1080
4
1
1081
4
1
1082
2
1
1083
4
3
1084
2
1
1085
2
1
1086
1
1
1087
4
3
1088
1
1
1089
3
1
1090
1
3
1091
1
3
den_
2000
(8)
3
2
4
4
4
4
3
2
1
4
4
2
3
4
2
3
3
2
2
4
4
road_
den
(17)
4
4
4
4
4
4
3
2
2
4
3
3
3
2
3
3
3
3
3
4
2
xing_
a
(17)
4
4
4
4
4
4
3
2
1
4
2
3
3
3
3
3
3
4
4
4
4
x1_
2_stm
(1)
4
4
4
4
4
4
2
3
1
3
3
2
3
2
3
2
2
4
3
3
3
b30_
rddn
(0.1)
4
4
4
4
4
4
2
2
2
3
2
2
2
3
3
4
2
3
3
4
3
no_
3_dep
(17)
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
res_
high
(2)
3
2
4
4
3
3
4
2
3
4
4
3
3
4
4
3
3
3
4
4
4
Mine
Human
(7)
4
4
4
3
3
1
4
4
1
4
1
1
3
1
4
1
4
4
3
1
1
(2)
4
3
3
4
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
Watershed
Scores
Category
385.4
385.4
417.4
441.4
350.4
425.4
317.2
260.2
277.2
360.3
288.2
262.2
367.2
263.3
286.3
258.4
373.2
290.3
308.3
361.4
327.3
very high
very high
very high
very high
high
very high
high
medium
medium
very high
medium
medium
very high
medium
medium
medium
very high
medium
high
very high
high
110
111
Table A10. Candidate metrics (50) analyzed for use in crayfish risk assessment. (* metrics used in final crayfish risk assessment) (more in-depth definitions of land use/land
cover data found in Table A3, and road data found in Table A2)
Metric
Definition
dam_a *
Dams per watershed per square kilometer of area in the watershed
res_sa_a
Amount of reservoir surface area produce by the dams in a watershed
divided by the total surface area of the watershed (square kilometers)
res_v_a
Amount of reservoir volume produce by the dams in a watershed
divided by the total surface area of the watershed (square kilometers)
res_age
Average age of the dams in the watershed
damxh_a
Average height of dams in the watershed divided by the total surface
area of the watershed (square kilometers)
pop_cat *
Population density categories for three time periods current (1990 –
2000), recent (1950-1990), and historic (1790-1940) for high,
medium, and low growth rates
den_2000 * Population density in 2000
road_den *
Road density in the watershed (roads per square kilometer of total
surface area in the watershed)
xing_a *
Total number of road/stream crossings in the watershed divided by the
total surface area of the watershed (square kilometers)
xing_stm
Number of road/stream crossings per stream mile divided by the total
surface area of the watershed (square kilometers)
x0_1_stm
Number of road/stream crossings per stream mile on streams with 01% slopes divided by the total surface area of the watershed (square
kilometers)
x1_2_stm * Number of road/stream crossings per stream mile on streams with 12% slopes divided by the total surface area of the watershed (square
kilometers)
b66_rddn
Roads per square kilometer of total surface area in the watershed
within 66 ft of stream
b66_pstm
Percentage of streams in the watershed with a road within 66 feet
b30_rddn * Roads per square kilometer of total surface area in the watershed
within 300 ft of stream
b30_pstm
Percentage of streams in the watershed with a road within 300 feet
b66_pg01
Percentage of streams in the watershed with 0-1% slopes and with a
road within 66 feet
b30_pg01
Percentage of streams in the watershed with 0-1% percent slopes and
with a road within 300 feet
b66_pg12
Percentage of streams in the watershed with 1-2% slopes and with a
road within 66 feet
b30_pg12
Percentage of streams in the watershed with 1-2% slopes and with a
road within 300 feet
no_3_dep * Average kilogram per hectare of nitrate deposition in the watershed
so_4_dep
Average kilogram per hectare of sulfate deposition in the watershed
water
Percentage of watershed designated as water by land use/land cover
data (class)
112
Table A10. continued.
Metric
Definition
res_low
Percentage of watershed designated as low residential by land
use/land cover data (class)
res_high *
Percentage of watershed designated as high residential by land
use/land cover data (class)
com_ind
Percentage of watershed designated as commericial/industrial by land
use/land cover data (class)
bare
Percentage of watershed designated as Barren by land use/land cover
data (class)
mine *
Percentage of watershed designated as mining by land use/land cover
data (class)
trans
Percentage of watershed designated as transitional by land use/land
cover data (class)
desfor
Percentage of watershed designated as deciduous forest by land
use/land cover data (class)
evefor
Percentage of watershed designated as evergreen forest by land
use/land cover data (class)
mixfor
Percentage of watershed designated as mixed forest (deciduous and
evergreen) by land use/land cover data (class)
shrub
Percentage of watershed designated as shrubland by land use/land
cover data (class)
nnat_wod
Percentage of watershed designated as non-natural woody (orchards,
vineyards, etc.) by land use/land cover data (class)
grass
Percentage of watershed designated as grassland by land use/land
cover data (class)
past
Percentage of watershed designated as pasture by land use/land cover
data (class)
crop
Percentage of watershed designated as cropland by land use/land
cover data (class)
grain
Percentage of watershed designated as grain agriculture by land
use/land cover data (class)
urec
Percentage of watershed designated as urban recreation by land
use/land cover data (class)
wwet
Percentage of watershed designated as woody wetland by land
use/land cover data (class)
hwet
Percentage of watershed designated as emergent herbaceous wetlands
by land use/land cover data (class)
watr_wet
Percentage of watershed designated as water, woody wetland, and
emergent herbaceous wetland by land use/land cover data (sub-group)
urban
Percentage of watershed designated as low residential, high
residential, urban recreational, and commercial/industrial by land
use/land cover data (sub-group)
bar_min
Percentage of watershed designated as barren, mines, and transitional
by land use/land cover data (sub-group)
113
Table A10. continued.
Metric
Definition
forest
Percentage of watershed designated as deciduous, evergreen, and
mixed forest by land use/land cover data (sub-group)
nshrub
Percentage of watershed designated as natural shrubland by land
use/land cover data (sub-group)
ngrass
Percentage of watershed designated as natural grassland by land
use/land cover data (sub-group)
agricult
Percentage of watershed designated as pasture, row crop, small grain,
orchards, and vineyards for agriculture by land use/land cover data
(sub-group)
natural
Percentage of watershed designated as water, wetlands, forest,
shrublands, grasslands, and barren by land use/land cover data (group)
human *
Percentage of watershed designated as developed, mining, transitional,
and agricultural by land use/land cover data (group)
114
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